{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4573b6c1-96c9-4f5c-b78e-52d503fbce02",
   "metadata": {},
   "source": [
    "# **1.3 商品聚类**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5cf8b79b-0838-4e8f-88bb-365a7a5f45ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "检查合并结果：\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 98666 entries, 0 to 98665\n",
      "Data columns (total 3 columns):\n",
      " #   Column               Non-Null Count  Dtype \n",
      "---  ------               --------------  ----- \n",
      " 0   order_id             98666 non-null  object\n",
      " 1   order_item_id        98666 non-null  int64 \n",
      " 2   shipping_limit_date  98666 non-null  object\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 2.3+ MB\n",
      "None\n",
      "-------------------分割线-------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 99440 entries, 0 to 99439\n",
      "Data columns (total 3 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   order_id       99440 non-null  object \n",
      " 1   payment_value  99440 non-null  float64\n",
      " 2   payment_type   99440 non-null  object \n",
      "dtypes: float64(1), object(2)\n",
      "memory usage: 2.3+ MB\n",
      "None\n",
      "-------------------分割线-------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 98198 entries, 0 to 98197\n",
      "Data columns (total 12 columns):\n",
      " #   Column                         Non-Null Count  Dtype  \n",
      "---  ------                         --------------  -----  \n",
      " 0   order_id                       98198 non-null  object \n",
      " 1   order_item_id                  98198 non-null  int64  \n",
      " 2   shipping_limit_date            98198 non-null  object \n",
      " 3   payment_value                  98198 non-null  float64\n",
      " 4   payment_type                   98198 non-null  object \n",
      " 5   customer_id                    98198 non-null  object \n",
      " 6   order_status                   98198 non-null  object \n",
      " 7   order_purchase_timestamp       98198 non-null  object \n",
      " 8   order_approved_at              98184 non-null  object \n",
      " 9   order_delivered_carrier_date   97581 non-null  object \n",
      " 10  order_delivered_customer_date  96469 non-null  object \n",
      " 11  order_estimated_delivery_date  98198 non-null  object \n",
      "dtypes: float64(1), int64(1), object(10)\n",
      "memory usage: 9.0+ MB\n",
      "None\n",
      "-------------------分割线-------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 98198 entries, 0 to 98197\n",
      "Data columns (total 16 columns):\n",
      " #   Column                         Non-Null Count  Dtype  \n",
      "---  ------                         --------------  -----  \n",
      " 0   order_id                       98198 non-null  object \n",
      " 1   order_item_id                  98198 non-null  int64  \n",
      " 2   shipping_limit_date            98198 non-null  object \n",
      " 3   payment_value                  98198 non-null  float64\n",
      " 4   payment_type                   98198 non-null  object \n",
      " 5   customer_id                    98198 non-null  object \n",
      " 6   order_status                   98198 non-null  object \n",
      " 7   order_purchase_timestamp       98198 non-null  object \n",
      " 8   order_approved_at              98184 non-null  object \n",
      " 9   order_delivered_carrier_date   97581 non-null  object \n",
      " 10  order_delivered_customer_date  96469 non-null  object \n",
      " 11  order_estimated_delivery_date  98198 non-null  object \n",
      " 12  customer_unique_id             98198 non-null  object \n",
      " 13  customer_zip_code_prefix       98198 non-null  int64  \n",
      " 14  customer_city                  98198 non-null  object \n",
      " 15  customer_state                 98198 non-null  object \n",
      "dtypes: float64(1), int64(2), object(13)\n",
      "memory usage: 12.0+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import KMeans\n",
    "deliver  = pd.read_csv('C:/Users/10528/Desktop/Projects/Brazilian E-Commerce Public Dataset by Olist/olist_orders_dataset.csv')\n",
    "order = pd.read_csv('C:/Users/10528/Desktop/Projects/Brazilian E-Commerce Public Dataset by Olist/olist_order_items_dataset.csv')\n",
    "pay = pd.read_csv('C:/Users/10528/Desktop/Projects/Brazilian E-Commerce Public Dataset by Olist/olist_order_payments_dataset.csv')\n",
    "customer = pd.read_csv('C:/Users/10528/Desktop/Projects/Brazilian E-Commerce Public Dataset by Olist/olist_customers_dataset.csv')\n",
    "product = pd.read_csv('C:/Users/10528/Desktop/Projects/Brazilian E-Commerce Public Dataset by Olist/olist_products_dataset.csv')\n",
    "review = pd.read_csv('C:/Users/10528/Desktop/Projects/Brazilian E-Commerce Public Dataset by Olist/olist_order_reviews_dataset.csv')\n",
    "#读取所需数据\n",
    "order1 = order.groupby(by= 'order_id').agg({'order_item_id':'sum', 'shipping_limit_date':'first'}).reset_index()\n",
    "print('检查合并结果：')\n",
    "print(order1.info())\n",
    "print('-------------------分割线-------------------')\n",
    "#根据order_id进行聚合计算，如商品购买数量求和，取首次商品送达限定时间，再将order1变成列\n",
    "pay1 = pay.groupby(by = 'order_id').agg({'payment_value':'sum', 'payment_type':'first'}).reset_index()\n",
    "print(pay1.info())\n",
    "print('-------------------分割线-------------------')\n",
    "#根据order_id进行聚合计算，如支付金额求和，取首次支付方式，再将pay1变成列\n",
    "deliver_happened = deliver[~((deliver.order_status == 'canceled')|(deliver.order_status == 'unavailable'))]\n",
    "#剔除递送状态为取消或未收到货的订单\n",
    "order2 = order1.merge(right = pay1,left_on = 'order_id',right_on = 'order_id').merge(right = deliver_happened,left_on = 'order_id',right_on = 'order_id')\n",
    "#将order1与pay1进行右外连接，再将连接后的表与deliver_happened进行右外连接，变成order2表\n",
    "print(order2.info())\n",
    "print('-------------------分割线-------------------')\n",
    "order2 = order2.merge(right = customer, left_on = 'customer_id',right_on = 'customer_id')\n",
    "#将order2与customer进行右外连接，生成新的order2\n",
    "print(order2.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "45c162e1-1aba-4335-aa49-aa910211e6e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "order2['shipping_limit_date'] = pd.to_datetime(order2['shipping_limit_date'])\n",
    "#将order2.shipping_limit_date转化为日期格式\n",
    "order2['ordermonth'] = order2.shipping_limit_date.values.astype('datetime64[M]')\n",
    "#定义ordermonth列，标记order2的每个订单的下单年月，方便作为筛选条件\n",
    "review1 = review.iloc[:,1:3]\n",
    "#获取review表个中前1、2列的所有条目\n",
    "review2 = review1.merge(right = pay1,left_on = 'order_id', right_on = 'order_id')\n",
    "#将review1和pay进行外右连接"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "64e7dac5-ee14-4ce4-afdd-189486a76810",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32951 entries, 0 to 32950\n",
      "Data columns (total 4 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   price          32789 non-null  float64\n",
      " 1   payment_value  32789 non-null  float64\n",
      " 2   order_item_id  32789 non-null  float64\n",
      " 3   review_score   32789 non-null  float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 1.0 MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "p = order.merge(right = review2, left_on = 'order_id', right_on = 'order_id').groupby('product_id').agg({\n",
    "'price': 'first','freight_value':'first','payment_value':'sum','order_item_id':'sum','review_score':'mean'\n",
    "})\n",
    "#将order与review2进行右外连接，再根据product_id进行分组，取首次的价格和运费价格，合计支付金额，计算订单平均评价分数\n",
    "p1 = product.merge(right = p, how = 'left', left_on = 'product_id',right_on = 'product_id')\n",
    "#将p与product表、行右外连接\n",
    "p1.price.fillna(p1.price.mean())\n",
    "#将p1.price一列NaN值进行平均价格填充\n",
    "x = p1.iloc[:,[9,11,12,13]]\n",
    "#取p1的第10、12、13、14列的所有行信息作为x，\n",
    "print(x.info())\n",
    "#x表的四列即为产品聚类的四个评价维度\n",
    "x = x.dropna()\n",
    "#剔除X中有NaN值的行信息\n",
    "ss = StandardScaler()\n",
    "#导入数据标准化方法，以免某个评价维度干扰到总体的聚类分析\n",
    "train_ss_x = ss.fit_transform(x)\n",
    "#将X中四列的所有数据进行数据标准化，标准化后每列数据：均值为 0，方差为 1,实现更平衡、更可靠的聚类和建模."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ae6a49d9-a3d4-4264-871b-af4d9d11e26a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NaN数量: 0\n"
     ]
    }
   ],
   "source": [
    "print(\"NaN数量:\", np.isnan(train_ss_x).sum())\n",
    "#检查是否存在空值情况\n",
    "model = KMeans(n_clusters = 8)\n",
    "#导入KMeans算法，先设置聚类数量为8，再利用肘部法则来确定最佳聚类数量\n",
    "model = model.fit(train_ss_x)\n",
    "#开始聚类训练train_ss_x 是你提前标准化后的特征数据；\n",
    "#KMeans 过程：\n",
    "#1.随机选择 8 个数据点作为“初始中心点”；\n",
    "#2.每个样本点“投票”给离自己最近的中心点（计算欧几里得距离）；\n",
    "#3.重新计算每类的中心点；\\\n",
    "#不断重复 2-3，直到中心点不再变化（收敛）或达到最大迭代次数。\n",
    "predict_y = model.predict(train_ss_x)\n",
    "#使用训练好的模型，给每个样本贴上一个类别标签（0 到 7）；\n",
    "#返回的 predict_y 是一个一维数组，表示每个样本属于哪一类。\n",
    "predict_y = pd.DataFrame(predict_y)\n",
    "#将数列predict_y变为DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1cf0c01b-0518-4866-abd9-6242df55fa18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "肘部法则判断结果如下所示：\n",
      "{2: 7968.929165192517,\n",
      " 3: 11717.027402138667,\n",
      " 4: 13909.534364062707,\n",
      " 5: 16020.800400725157,\n",
      " 6: 15560.528447382996,\n",
      " 7: 16203.405635178488,\n",
      " 8: 17241.248881687723,\n",
      " 9: 17012.960224398696,\n",
      " 10: 16747.696626269695,\n",
      " 11: 16354.672817362652,\n",
      " 12: 16228.466503117179,\n",
      " 13: 16591.825862606234,\n",
      " 14: 16076.59251860236}\n"
     ]
    }
   ],
   "source": [
    "import pprint\n",
    "from sklearn.metrics import calinski_harabasz_score\n",
    "#引入肘部法则（Elbow Method），判断聚类数量最佳值（类间距离越大，类内距离越小，分数越高 ⇒ 聚类效果越好）。\n",
    "score = {}\n",
    "for i in range(2,15):\n",
    "    model = KMeans(n_clusters = i)\n",
    "    model = model.fit(train_ss_x)\n",
    "    predict_y = model.predict(train_ss_x)\n",
    "    score[i] = (calinski_harabasz_score(train_ss_x,predict_y))\n",
    "print('肘部法则判断结果如下所示：')\n",
    "pprint.pprint(score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b6d9cbc-9ace-45eb-8723-e7a1c3f9d0f4",
   "metadata": {},
   "source": [
    "### **结论：n = 8的值最高，因此将聚类数量设置为8是最理想的分类（注：该步骤目的是找出理想分类数量，得到数量后需回到上一步进行分类计算）**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "1a8d3799-a50d-437e-8665-e22bd956bbc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "分类后的四个维度结果如下：\n",
      "       price             payment_value              order_item_id            review_score          \n",
      "       count        mean         count         mean         count       mean        count      mean\n",
      "label                                                                                              \n",
      "0.0    16612  142.647418         16612   601.005973         16612   4.007826        16612  4.062515\n",
      "1.0     3740  151.054471          3740   612.805634          3740   4.059091         3740  4.032548\n",
      "2.0       14  105.987143            14  1160.769286            14  12.428571           14  3.825209\n",
      "3.0      863  149.691981           863   594.184438           863   4.113557          863  4.014942\n",
      "4.0      195  161.493590           195   676.115128           195   3.789744          195  3.885051\n",
      "5.0     9713  145.263959          9713   629.593070          9713   4.188510         9713  4.036460\n",
      "6.0     1442  157.585180          1442   684.845555          1442   4.608183         1442  4.035681\n",
      "7.0       48  122.569792            48  1528.831042            48   4.458333           48  4.109002\n"
     ]
    }
   ],
   "source": [
    "x['cluster_label'] = predict_y\n",
    "#聚类结果赋给x的新列cluster_label\n",
    "result = x\n",
    "result.columns = ['price','payment_value', 'order_item_id','review_score', 'label']\n",
    "pd.set_option('display.max_columns',None)\n",
    "result = result.groupby('label').agg(['count','mean'])\n",
    "#根据label分类，对四个维度分别进行计数和均值计算\n",
    "pd.set_option('display.max_columns', None) \n",
    "pd.set_option('display.width', 1000)\n",
    "print('分类后的四个维度结果如下：')\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5109c6dc-04aa-4b74-ad9b-cfe17b66cb24",
   "metadata": {},
   "source": [
    "**优质商品：** 1、6\n",
    "\n",
    "**高价商品：** 2、7\n",
    "\n",
    "**热销商品：** 0、5\n",
    "\n",
    "**普通商品：** 3\n",
    "\n",
    "**差评商品：** 4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9c1bb8e-db19-4eb5-b682-877975baf2c2",
   "metadata": {},
   "source": [
    "### **聚类可视化**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "f2cbd183-c707-4a24-8930-6e284aa9419a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/P1/913/9l2ppaVGGYajHH388fn7fFIJoNKpmz56tbr/9drVkyRJ18cUXq8zMTDVq1Cj15JNPqv/7v/9T9913nzr77LOV2+1WQPzf0F133aX+/e9/qxNOOEG98sor6rXXXlOjR49W11xzjXI4HCo9PV2lp6crTdOUx+NRKSkpas6cOR36et1113U6f7c3XnrpJXXaaad9rWsIMVTICKwQg1xZWRlXX301jz32GNu3b+9wftq0aWzbto0lS5YwevRo0tPT4+f++Mc/YhgGixcv5thjj+U//uM/WLBgAU888US8zbnnnsuzzz7Ld77zHU4++WROPPFEPvnkk4R71NbW8tZbb3Haaadx2223MXny5Pi0hYqKCkzT5Pvf/z5HH300Cxcu5PXXX2f8+PFJv8b8/HxCoRB33nkn9913Hx9//DGbNm3iggsu4IQTTsDn8zFv3jxeeeUVMjIyeOSRR7j55pt58cUX4x9bl5eXM3/+fLxeL+vXr+e2225D13VGjRrFqFGj4vVFS0pKeO+99/jBD37QYfSzoaGBG2+8kTvuuIOJEyfGjxcUFJCWlobL5Up4pKamkpGRkfA+1dXVsXLlSnw+X/yx796ff/55wnGfz0c0Go2XqOoL4XCY8847j3Xr1hEOhzn77LP5xz/+kVCuq7y8PGHEc/9zOTk5rF27luuuu67DtYPBIL/+9a+B9hJTjz/+OHa7/Wv1V9f1LkesX3/9dTRNw+12c/zxx3P88cezaNGihD7rete/xtasWUNNTQ3HHXccaWlpzJgxo9NpBDabjQcffJDf/OY3+Hw+mpqaWLRoEatXr+aMM87g/vvvJy0tjccff5ytW7fy2muvxac83HbbbeTm5tLS0sK3v/3t+Eiv3W7nkksuobm5mebmZvLy8njhhRfw+Xx8+umnHfrQ0NBAXl4eAIWFhT1OI9i/1vE+pml+pWkcQgxFtoHugBCie3/4wx+YOXMmjz/+OHfeeSePPvpowvnRo0fj9Xp58sknO0wfWLt2LXV1dR0+3i0sLIz///PPP5/HH3+cu+66i08//ZTq6mpGjRqV0P6yyy4jNTU1/nVubm68kP28efM49NBD47+8Z8+ezVlnncWYMWOSfo3FxcU8/vjjjB8/nqqqKpxOJ3l5eeTm5vLss88yb948jjvuOEaNGkV2dja7du2ivLycSZMmxa+RlpbGhx9+SG5uLpqmUVFRAcCePXvYvXs3O3fuTPj4/ZZbbiESiVBdXU1BQQGbNm3i4osvpq2tDa/Xy/XXX8/mzZt57rnnuO666/jLX/7Sad8feOABrrrqKgB+8IMf8PLLL3f5OmfNmtXp8SuuuKLT6++bOwvt9T57Eg6HOe6449i1axff/e53SUlJ4Y477uDFF1/khhtu4Mc//jF+v59ly5Zhs9koKSnhlFNOic8T3mfEiBEdrv3CCy9w00030dbWBpAw9/TrKCwsTKhhu7958+bxf//3f9hsNsaMGUNKSkr83L6fR7/fnzAH+eSTT+aSSy7hkksu4c033wSIz3OG9j+4lFId5vbOmDGDvLw8du3axZVXXslZZ53Fj3/84/j5/aeK3H///ZxyyikANDY2cvnll3PHHXewefNmKisr4/eLxWLxOctKKYLBIC0tLcRiMbKzsxPuv2vXLo488kgAnE4n9957L9/73vc6vCdLlizhnHPO6XTKRlVVVcK/bSGGMxmBFWKQy8/PJy8vj2uuuYa//vWvbN26tUObqVOn8u9//7tDgAU4/PDDWblyZcJj3yhUW1sbM2fO5MEHH2Tu3Lk88cQT3H///R2uUVZW1mX/HA4Hn376KU899RRjx47lySefZOLEibz33nu9ep1ut5v333+fiy66iMcee4wdO3bw+eefU1ZWRmpqKtdffz1HH300L774IrfffnuHjQguv/xyjj76aKZMmcLUqVPjj29/+9sAzJ8/P+H41KlTmTJlSry4/NKlS1m2bBkA//rXvwgGg5x00km43W7cbjdnnXUWwWAw4TFq1Kj4fFWAJ554Ap/Ph2maKKVYvnw5aWlp8T863nvvPXJycnjmmWdQSmGaJk1NTQnzdPdXWFgYf1x44YUAXS78gvbg8+1vf5uPPvooPsJ666238vjjj3PooYdy880389RTT5Gbm4vdbmf79u0cffTRnHvuud1+b37/+99z5plnMm/evPhc3L5SWFiIrusd5iQDeL1eZsyYwdSpUxPCK8C4ceMAEj6ViEQivPXWW/HQ+MYbb3DMMcfEf+7/8pe/UFNT02GeaFNTE6+//jpVVVW43W7S09Opra3FsiyUUsyaNYtHH30UpRShUIgrrrgi/tybb76ZjRs3cvHFFzNjxoyE+d+LFy+OL0isq6vj/PPPZ8SIEfGguo9SitWrVzN9+nSgfS5venp6h00WCgoKyMrK6nKU9b333uOwww7r8T0XYjiQACvEEHHrrbficrn45S9/2eHcvuC67xfgPlOnTmX37t1MmjSJGTNmMGPGDDZt2sQDDzwAwDvvvMO2bdt4/vnnufXWWznxxBPZsWNHh+t397Hke++9x+9//3tOPfVU7rzzTpYtW8aYMWM6jBR3x7IsFi9ezAknnEB1dTUTJ07kpz/9aXy0b8+ePZSVlTFr1iwmTJjA7t27+fnPf55wjbfffhu/309ra2v8Y9u9e/dSWFjI5MmTKS4uZuvWrfFzbW1tRCIR/vjHPwJw9tlns2nTJpqbm3nvvfd48MEHufHGG4H2j6l1Xe8whWDfivB9srKy4juKPf3005x44onceOONnHnmmQBkZ2dz3333cdlll/Gb3/wGy7LIyMhImIaw7/2AxEVcH3zwAWVlZT1u4fr//t//o7S0NP71vsVn0P4Hy5133slVV10Vf037HDidYn9XX301H3zwAU888QRpaWmdttl/F6re0DSNs88+m7/+9a+9et6+EdB///vf8WNLliyJB85QKMRHH33EySefHP/ZP//887HZbB2mEbz//vuceuqpHHfcccyZM4dvfetb7NixA7/fj8/nw7KseHWLSCRCa2tr/CP8G264gddee436+noCgUDCCHxXUwgO3MVs6dKltLW1ceyxx8bfk95qaWnhtdde46yzzur1c4UYiiTACjFE5Obmcu2117J48eIOvwD3BdgDR2AXLlxIOBzm/PPP5+2332bx4sVcc8018eCSk5MDwOOPP87777/Pddddx29+8xtisVjS/bLb7dx+++3ce++9LFmyhMcee4xdu3Z1O2p7oAULFnDzzTfzy1/+knvvvZdLLrmE559/nvLycgCuu+46/vznP+N0Onn33XdZtmxZjyv9g8EgV155JS6Xi+XLl3PYYYdx0kknsWHDhk7bezwexowZw86dO3njjTf44x//yLXXXhufv2qaZof5qweGvnA4zAsvvMBRRx3FVVddxT333MMvfvGLhDbnnnsuTz/9NP/7v/9LSUkJt99+O0uWLEloM2rUqA6VHI4++mi2bt3aYSQyWZZl8aMf/QjLsrjhhhs69HvGjBmsWrUq4fi+8OxwODj66KO7vf5NN93EOeec85X6dtNNN/HAAw+wa9eupJ8zbtw4rrzySu68807uvfdeXnrpJa688kqOOOIIDj/8cD766COCwWBCv1NSUjj00EPjAXbf9++0005jw4YNvPXWW/E/4ObMmRMvkbZy5Up+/OMf4/V6SUtLIycnJ/49mzx5MlOmTGHTpk08+uij3HTTTTz00ENd9lspRTgcxu/3x4898sgjHHbYYfGpG939MdHV+Z/97GdcfPHFHaYmCDFsHdw1Y0KIZHW2lWxdXZ1KTU1V559/frwKwY4dO9T777+vHA6Hikaj6tFHH00oiL5ixQp1/PHHK5fLpYqKitStt96aUET9zjvvVPn5+SozM1N95zvfUf/7v/+rdF1X27ZtU0q1VyF49NFHE/p27LHHJuw09cQTT6hp06Ypt9sdXyl+4Paq3amqqlKbNm1SV199tXI6neqGG26IVw2YPn16fOV4W1ub+s///E/l9XpVRkaG+t73vteheH44HFbPPPOMmjp1qpoyZUp8a9pYLKZuueUW5XQ61cKFCztshHD22WcrTdMUoLKzs9URRxyhLrnkEtXS0qIWLlzY5Y5KTzzxRPwa1157rXI4HOqHP/yhqqqqih+vr69XgFq+fHn8WFtbm7rrrrvU6NGj1Y9//OOk36t9Fi5cqPLy8ro8/5Of/ER9//vfV0opFQqF1KWXXqp0XY9Xj/jOd76jLrzwQrVz507197//XQFq48aNCdf4xS9+oY466qiEY7t27YpXv9ixY4fasWOHWrdunRo1apQ68cQTe/069rn77rvVEUccEf++d7aRwYGi0aj62c9+pvLz85Xb7VbnnntufNewW2+9VTkcjg5VHm6++WblcrlUIBBQq1at6lDtoTOzZs2K/xuwLEuFQiEVi8VUY2Ojstvt8U0qjjrqKHXrrbeqpUuXqoULF3bYzGLfBhbst9nIp59+qux2u7r//vvj9yspKelx97f9qy88++yzauTIkaqpqanH1yLEcCEBVggxKNx+++1qzpw56pNPPkk4Pm7cuIRf7kop1draqh5//HH10ksvxY89/PDDav78+SorK0ulpaWpn/70px12yFKqfeekGTNmKEBNnDhRvf7660oppZYvX65ee+01VV1d3eE5P/zhD9U555zT4fjo0aMTthatr69Xe/fu7dCuoqJCAR1em1JKmaaZsJ1tT8455xw1c+ZMpet6t4Fx4cKF6qKLLlK1tbVq2rRpClC/+c1v4ud/97vfKV3X44Howgsv7HCNn/70p+qwww5LOGZZljr22GM7BKrs7Gz19ttvJ/06DmRZlrrwwgvVHXfc8ZWv0VtLly5NKsDOmDEjofTc/n72s5+pJ554QjU3Nyccv/baa9UVV1zR6XNM04z/gbd79271xz/+MeGPyqKiovhubwc+XnnlFQXEtwJubGxUBQUFasWKFUm9ZiGGC02pHj6rEEKIg0R1sjo8WS+//DKPP/448+fP56yzzkqomtDZfd58802effZZ/vjHP+J0Oru99r4FPQUFBV+pb33p7rvvZvXq1Rx66KFcdtllXW5IcNVVV9HQ0MCzzz7LDTfcwOjRo/nJT37SZ/2IRCLxubr7difri2tGo9GvPE2itz755BPmzZvX40f2EydOZOHChVx77bVJX/vKK68E6LJ6RXf27NlDZmZmtz/D+2tsbCQrK6vX9xFiKJMAK4QQQgghhhRZxCWEEEIIIYYUCbBCCCGEEGJIkQArhBBCCCGGFAmwQgghhBBiSLENdAcOBsuyqKysxOv1fuUVzkIIIYQQov8opWhra6OoqChhp8DOfCMCbGVlJSNHjhzobgghhBBCiB7s2bOH4uLibtt8IwKs1+sF2t+QrvbxFkIIIYQQA6e1tZWRI0fGc1t3vhEBdt+0gbS0NAmwQgghhBCDWDLTPWURlxBCCCGEGFIkwAohhBBCiCFFAqwQQgghhBhSvhFzYIUQQgghBjOlFLFYDNM0B7or/cput2MYxte+jgRYIYQQQogBFIlEqKqqIhAIDHRX+p2maRQXF5Oamvq1riMBVgghhBBigFiWxY4dOzAMg6KiIhwOx7DddEkpRV1dHRUVFYwbN+5rjcRKgBVCCCGEGCCRSATLshg5ciQej2egu9PvcnNz2blzJ9Fo9GsFWFnEJYQQQggxwHraOnW46KvR5W/GuyWEEEIIIYYNCbBCCCGEEGJIkQArhBBCCDFE7dy5c9gu+uqOBFjRKypmYUVNlKXa/39keNerE0IIIb5pSkpKeO+99wa6G92SKgQiKcq0UDGLwOpqgqurMdvCaHYDV1kWKXOL0VOd6I6vX5hYCCGEEKInMgIreqRMi1hjkLqHluF7fydmcwhMhQrFCK6vpf7hFQRWVMporBBCCNEPVq5cyRFHHEFqaipHHnkk69ev77b9e++9R0lJScIxTdPYuXMnAG+88QaTJk3C4/Fw5JFHsm3bNgBOPfVUNE1j165dHH/88Wiaxt133x2/xmuvvca0adPIyMjgyiuvJBwOx8+VlJTw1ltv8bOf/YyCggJWr17dNy++CxJgRY9U1KLx6bWoYKzLNr4PdxHe1ogyrYPYMyGEEGJ4a21t5dRTT+WMM85g06ZNzJ07l4suuuhrXfPSSy/liiuuYPPmzUydOpU77rgDgH/+8580NTUxcuRIXnzxRZqamvjJT34CwLZt21iwYAE/+clPWL58OcuXL+c3v/lNwnV//vOfU1lZyeLFiykrK/tafeyJTCEQ3VIxE/+KSlSo6/C6j+/j3bjGZoHMJBBCCCH6xEsvvURWVha33XYbAHfccQeHH37417qm2+0mHA6Tnp7OAw88gGW1Dz6lpKQA7TVpU1NTycjIiD9n8eLFzJw5kx/84AcA/PjHP+bhhx+Oh1+A9PR0Hnvssa/Vt2TJCKzogUZwdXVSLc2mILGmUD/3RwghhPjmqKioSJgOkJmZyfe+971eXSMQCCR8vXjxYt577z0KCws56qijWLFiRY/X2Lt3LytWrCAjI4OMjAxuuukmdu/endBm4cKFverX1yEBVnRP17B8kaSbmy0SYIUQQoi+MnLkSHbs2BH/2ufzMXXqVKqrux5c0jQN0/xyXcqyZcvi/9/v9+P3+3nzzTdpbGzk6KOPjo+q7qPrOkqphGPFxcWcccYZrFq1ilWrVrF69WrefPPNhDb7RnAPBgmwomd68vXlNJv8SAkhhBB95bTTTqOpqYm77rqLiooKfvWrX2GaJvn5+V0+p7i4mKqqKrZt24bf7+e//uu/4ucsy+K0007jb3/7G/X19ei6Hp9CsM/YsWN57bXXqKqq4u233wbgggsu4MMPP2TLli0A/P73v+fyyy/v+xecJEkbolsqauIszUyusU3HPiKtfzskhBBCfIOkpaXx2muv8eKLLzJp0iQ+/fRT/vWvf3W7eUFZWRk33HADRx11FEcddVTCPFWv18vf/vY3/vu//5uysjJefPFF7r///oTn//a3v+W1115jzJgx/PKXvwSgtLSUxx9/nBtvvJEpU6awbt06Fi9e3D8vOgmaOnCMeIC98MIL/OQnP2H37t3MmjWLxx57jEmTJrFw4UL+9Kc/xduVlZWxdevWpK7Z2tpKeno6LS0tpKVJwOqtSGUrjU+vxVmahZHqQJkW0Vo/sWpfQjv31Dy8J5RJPVghhBAiSaFQiB07djBmzBhcLtdAd6ffdfd6e5PXBtUI7LZt27j88su5++672bt3L6NHj+bKK68EYPny5bz88ss0NTXR1NTEypUrB7i33xy2nBTyrpmDZ0YBRpYbe4GXjNMnkH3JDFwTcgAwMl14v1Uq4VUIIYQQ/W5QldEqLy/nrrvu4rzzzgPg6quv5tRTTyUWi7Fu3TqOOeYYUlNTB7iX3yxWxCS8vRHfh7vaNzDYj6Mkg7QTSnFOyMZZkolml/AqhBBCiP43qEZgTz/9dH784x/Hv960aRNjx45lzZo1KKWYMWMGbrebU089tUPpBtH3rIhJcG0NLS9u6hBeASI7m2l4ag32Ai+aw0DrxWIvIYQQQoivalAF2P1FIhF++9vfcs0111BeXs6UKVNYvHgxGzZswG63c9VVV3X53HA4TGtra8JD9J6KmLS9s737NsEYLS9vRkVlG1khhBBCHByDagrB/u644w5SU1P50Y9+hN1uT9g27U9/+hOlpaW0trZ2Osl30aJF8VVz4quxoib+ZXuTahvd24oViKE7Bu2PkxBCCCGGkUE5Avvmm2/ywAMP8NRTT2G32zucz8jIwLIsqqqqOn3+bbfdRktLS/yxZ8+e/u7y8GMpwtsak24e2lzfj50RQgghhPjSoBsy2759OxdddBH3338/kydPBuDGG29k7ty58cVdS5cuRdd1Ro4c2ek1nE4nTqfzoPV5WNI0VMzqud0XVNREKdVtXTohhBBC9B9LWURMk5iyCMQieGwObJqOwzDQtUE5ZvmVDaoAGwwGOf300znzzDNZsGABPl97ndFDDjmE22+/nYKCAmKxGAsXLuSyyy7D4/EMcI+HMaWwpbuItIaTam7L9kh4FUIIIQZI2IyyqbmWV/esZ3vbl5+Klnpz+PbIKUzIyMNpdPxUe6gaVHH89ddfp7y8nIceegiv1xt/HHvssXz3u99lwYIFXHbZZZx88sn84Q9/GOjuDmua3cBzaFFybR0GrrKsfu6REEIIIToTNqO8sns9/7fh/YTwCrC9rZ7/2/A+r+xeT9iM9sv9161bx+zZs8nMzOSWW27hYOyRNagC7JlnnolSqsOjpKSERYsW0dTUxO7du/n9739PSkrKQHd3WNN0DWdpJkZmz7uCeGaPYHDt5yaEEEJ8M1jKYlNzLa9VbOi23WsVG9jcUoulkp8emIxwOMz8+fOZNWsWy5YtY8OGDTz22GN9eo/ODKoAKwYZXSPrgukYGV2HWPe0fFJmj5AduIQQQogBEDFNXt2zPqm2r+5ZT8Tq27KXr776Ki0tLdxzzz2UlZVx11138fDDD/fpPTozqObAisFF0zV0t52cy2YS2lSPf3klZlMQDB3HqHRS5xRjZHvQZQcuIYQQYkDElNVh2kBXtrXWE7Ms6MNf26tXr2bu3LnxdUnTp09nw4buR4P7ggRY0S1N10A3cE3KxTk+B83QQIEyLXSn/PgIIYQQAykQi/SqfTAWIdXed5WaWltbGTNmTPxrTdMwDIOmpiYyMzP77D4HkikEIimaoaM7DDRDR7PpEl6FEEKIQcBjc/SqvbuX7Xtis9k6lC51uVwEAoE+vc+BJMAKIYQQQgxRNk2n1JuTVNuytBxset9Gv6ysLOrq6hKOtbW14XD0bVA+kARYIYQQQoghymEYfHvklKTafnvkFBx6365bmT17Np9++mn86507dxIOh8nK6t/ymhJghRBCCCGGKF3TmZCRx6nFk7ttd2rxZMan5/f5jlzHHHMMLS0tPPHEEwDcfffdnHjiiRhG/y7wlomMQgghhBBDmNOw851RUyhLz+W1PevZ1vplVYKytPaduMan5+M0+j722Ww2HnzwQS688EJuueUWTNPk/fff7/P7dLhvv99BCCGEEEL0K6dhZ2pmIePT84hZFsFYBLfNgU3XcehGn4+87u/MM89ky5YtLFu2jHnz5pGbm9tv99pHAqwQQgghxDCgazouQweDPi2VlYwRI0YwYsSIg3Y/mQMrhBBCCCGGFAmwQgghhBBiSJEAK4QQQgghhhQJsEIIIYQQYkiRACuEEEIIIYYUqUIghBBCCDEMKMuCWATMGIQD4PSAYQObA62Pt5AdaBJghRBCCCGGOBUJoyo2oj57Gaq2fXmisAxtzmlQPBHNcXBLa/Wn4RXHhRBCCCG+YVQkjPrsJdTzf0gMrwBV21DP/6H9fCTcb31oaGhgzJgx7Ny5s9/usT8JsEIIIYQQQ5SyrPaR16WvdN9u6StQsbF9mkEfq6+v5/TTTz9o4RUkwAohhBBCDF2xSPu0gSRYn7/SPke2j51//vmcf/75fX7d7kiAFUIIIYQYqsxYx2kDXanc2t6+jz344INcf/31fX7d7kiAFUIIIYQYqsKB/m2fhNLS0j6/Zk8kwAohhBBCDFVOT/+2H6QkwAohhBBCDFWGDQrLkmtbNLa9/TAgAVYIIYQQYqiyOdrrvCZBP/w7YHP0c4cODgmwQgghhBBDlKbraMUT0WZ/p/t2s7/TvpnBMNmRa3iMIwshhBBCfENpDifMOR1txNj2UlmVW788WTS2feR1mO3EJQFWCCGEEGKI0xxOVMk09OIJ7aWywoH2BVuGrX2awUEYeVVK9fs99pEAK4QQQggxDGi6Dg5X+xfu1IHtTD8bHhMhhBBCCCHEN4YEWCGEEEIIMaRIgBVCCCGEEEOKBFghhBBCCDGkSIAVQgghhBBDilQhEEIIIYQYBixLETMtTFMRjsRwOmwYhobN0NF1baC716ckwAohhBBCDHGRqElFdRufra2iqs4fP16Ym8KcaYUUF3hx2I0B7GHfkikEQgghhBBDWCRq8tmaKp5/Z2tCeAWoqvPz/Dtb+WxNFZGo2S/3f+GFFygtLcVmszFnzhzKy8v75T77kwArhBBCCDFEWZaiorqNpeuqu223dF01FTVtWFbf7pa1bds2Lr/8cu6++2727t3L6NGjufLKK/v0Hp2RACuEEEIIMUTFTIvP1lYl1fbzNdXETKtP719eXs5dd93FeeedR35+PldffTXLli3r03t0RubACiGEEEIMUaapOkwb6EplnQ/TVGDvu/uffvrpCV9v2rSJsWPH9t0NuiAjsEIIIYQQQ1Q4Eutd+2jv2vdGJBLht7/9Lddcc02/3WMfCbBCCCGEEEOU09G7D9Od9v778P2OO+4gNTWVH/3oR/12j30GXYDtaiXbunXrmD17NpmZmdxyyy0o1beTkIUQQgghhhrD0CjMTUmqbVFuKobRP/Vg33zzTR544AGeeuop7PY+nKPQhUEVYLtayRYOh5k/fz6zZs1i2bJlbNiwgccee2yguyuEEEIIMaBshs6caYVJtT18egE2o++j3/bt27nooou4//77mTx5cp9fvzODKsB2tZLt1VdfpaWlhXvuuYeysjLuuusuHn744YHurhBCCCHEgNJ1jeICL7OnFnTbbvbUAorzvX2+I1cwGOT000/nzDPPZMGCBfh8Pnw+X79/Uj6oqhB0tZJt9erVzJ07F4/HA8D06dPZsGFDl9cJh8OEw+H4162trf3TYSGEEEKIAeawG8yZXsiI/FQ+X1NNZZ0vfq4oN5XDp7eH1/7Yiev111+nvLyc8vJyHnroofjxHTt2UFJS0uf322dQBdj97VvJ9pOf/ITt27czZsyY+DlN0zAMg6amJjIzMzs8d9GiRfzyl788mN0VQgghhBgwDrtBSVE6xfleTFMRjsZw2m0YhobN0Pt85HWfM888c0DWJQ2qKQT7238lm81mw+l0Jpx3uVwEAoFOn3vbbbfR0tISf+zZs+dgdFkIIYQQYsDouobDbuB22cjwunC7bDjsRr+F14E0KEdg961k+/TTT7Hb7WRlZbFu3bqENm1tbTgcjk6f73Q6OwReIYQQQggxPAy6EdjOVrLNnj2bTz/9NN5m586dhMNhsrKyBqqbQgghhBBigAyqANvVSrajjz6alpYWnnjiCQDuvvtuTjzxRAyj7ycjCyGEEEKIwW1QTSHobiXbgw8+yIUXXsgtt9yCaZq8//77A9hTIYQQQggxUAZVgO1uJVtJSQlbtmxh2bJlzJs3j9zc3IPcOyGEEEIIMRgMqgDbkxEjRjBixIiB7oYQQgghxKCjLIWKWWBaWKEYussGho5m09GGWSWCIRVghRBCCCFER1bEJLKnBf+ne4hWtsWP24u8pMwdiWNkOrqj/9YONTQ0sGnTJsaPH09OTk6/3WefQbWISwghhBBC9I4VMfF9uofm5zYkhFeAaGUbzc9twPfpHqyI2S/3f/rppxk7dizXXnsto0aN4umnn+6X++xPAqwQQgghxBClLEVkTwuBzyq6bRf4rILInhaU1be7ZjU3N7Nw4UI+/PBDVq5cyZ///Gf+8z//s0/v0RkJsEIIIYQQQ5SKWfg/TW7HUf9ne9rnyPahtrY27r33XqZOnQrAIYccQlNTU5/eozMSYL+BlGlhRcw+/yEWQgghxEFmWh2mDXQlurcNzL793T9y5Eguuuii9utHo/z2t7/l7LPP7tN7dEYWcX2DWJEYVjBGcE01ViCK7rLhnlaAnupAs+to2vBaoSiEEEIMd1Yo1rv24Ri6297n/Vi9ejXHH388DoeDjRs39vn1DyQjsN8ASimsUIzmFzZS/+Ay/J9WEFxTg//zvdQ/vJymZ9dhBaMoS0ZkhRBCiKFEd/VuLFJ39s/Y5fTp03n77beZMmUKl19+eb/cY38SYL8BVNSi8ak1RHY2d3o+ureNxr+uRkUkwAohhBBDiqFjL/Im1dQ+wgtG/0Q/TdOYOXMmjz32GC+88EK/z4OVADvMqZhFYEUlsYZAt+3M1jC+Jbuxov1TYkMIIYQQfU+z6aTMHZlU25Q5I9FsfRv93nnnHW655Zb41zZb+wivrvdvxJQA+w0QWFWVVLvguhpkFqwQQggxdGi6hmNkOp45xd2288wpxjEyvc935Jo4cSJ//vOfefDBB9mzZw8//elPOfnkk0lPT+/T+xxIAuwwZ4VjWG2RpNqqsInZFu7nHgkhhBCiL+kOg9S5I8k4e3L7NIH92Ed4yTh7MqlzR/bLTlxFRUX84x//4N5772XKlCkEAgH++te/9vl9DiRVCIY71cuCxX1b31gIIYQQB4HuMHCOycQxMh1Mq73agNMGho5m0/t85HV/p5xyChs2bOi363dGAuwwZkViaA4DzWmgwiaa00B321FRE8sf7fgEQ0P3Og9+R4UQQgjxtWm6huYwAKNfSmUNJhJghzFN1whtbcR7/BiMNBf2glSsYBTNbqDCMQKrqgmuq0GF2xduuSbkIEOwQgghhBjsJMAOZ5qGkepAc9nwf7KHpmcb4Is9kO1FXjwzC/EcWkTTP9dj+SKkHjkK3SE/EkIIIYQY3CStDGMqZmG2hmn5+7p4cN0nWtlGS2Ubril5ZJ03FdMXAalBIIQQQoghQKoQDGNWMEbLq1s6hNf9hdbXEtrSAEphpDoOYu+EEEIIIb4aCbDDlBUx8X+2p9vwuo9/6V5s2R5k/qsQQgghhgKZQjBMaTadUHldUm2t1jBmcwh7fmo/90oIIYQQ/cVSFqYKY1lRIlYAh+5B1+0YmhNdG15jlhJghytLoaJW0s1Nf4ThXXBDCCGEGL6iVojawHo2NDxHfWhz/HiOazyTs88mzzMFu+7q1z6ceuqpnH/++Vx22WX9eh+QKQTDl66B0b4oy16c1l5xYFYRzvHZ8eMJzV0SX4UQQoihKGqFWN/wTz7Ye3dCeAWoD23mg713s77hn0StUL/14cknn+T111/vt+sfSEZghykVM/F+qxTnyHSUpYjsaQFL4SzNJO3EMoLravB/sgcVtdA9dux5KQPdZSGEEEL0kqUsagPrKW98vtt25Y3Pk+ueSGHKzD6fTtDY2MhNN93EhAkT+vS63ZEAO0xpuoZzZDotr28lurc14ZyR4SL1yFFknjeNpn+swz2zEKWUFNESQgghhhhThdnQ8FxSbTc0/os892R0w92nfbjppps466yzCAaDfXrd7sgUgmFImRZmS5iGv67uEF4BzOYQLS9vJlrdRuY5k0mZPQLdbgxAT4UQQgjxdVhWtMO0ga7UBzdhqU62kv8a3n33Xd5++23+53/+p0+v2xMJsMORpWj+90ZU1Oy2Wdu7O7BleyS8CiGEEENUxAr0a/vuhEIhrrrqKu6//37S0tL67LrJkAA7DMUag8Tqk/gBtRT+lVWoWPdBVwghhBCDk0P39Gv77tx5553Mnj2b0047rc+umSyZAzvMKEsR2lSfdPvwtkZSDi1Ck58EIYQQYsjRdTs5rvFJTSPIcU9A1/qu6tBTTz1FXV0dGRkZAAQCAZ555hk+//xz7rvvvj67T2cktgw3lkKZydd/JWYhq7eEEEKIocnQnEzOPpsP9t7dY9vJWWdh6M4+u/eHH35ILBaLf33zzTczd+7cg1IHVgLscGNo2LKS/3jAyOjfosZCCCGE6D+6ppPnmcKkrDO7LaU1KetM8j1T+rSEVnFxccLXqamp5OTkkJOT02f36IoE2GFG0zRck3JpfWd7++hqD1JmjUB3yo+BEEIIMVTZdRdTss8h1z2RDY3/oj64KX4uxz2ByVlnke+Zgq2fd+J67LHH+vX6+5PkMiwpPIcWEvh8b7etbPkp2ItSD1KfhBBCCNFf7LqLwpSZ5HkmY1lRIlYAh+5B1+wYurPPNy8YaBJghyHdYcM7bxSWP0JofV2nbWy5KWSdNw3NJiW0hBBCiOFA13R0zQ26GycHt6zVwSYBdpjS7AbpJ43Fc2gR/s8qiFa0okwLW04KKYcV4SzNAkNWbwkhhBBi6JEAOwwp00JZCrMljGbTSTuxDN1hgKahTAvNbqDpEl6FEEIIMTRJgB1mrKhJqLwO/2cVmM2h+HHd68RzaCGemYUSXoUQQggxpEmAHUasqEnbO9sJrqnpeK4tjO/9nUQr20g/bbxsHyuEEEKIIWt4LUn7BlOWIrKzudPwur/wlgaCa2qwIrJ9rBBCCCGGJhmBHSZUzML/eQUAeqoD55hMNKeBCpuEdzVjtYbjbQMrKnFPyx+orgohhBCiPygLVBBUFMw2MLyg2UFzwzArozW8Xs03mVKoiEnGgonkXDYTx8h0jFQnjuI0ci6ZQcaZk7DlpQBgNoew2sKoqIzCCiGEEMOCFQD/R1BxDWw7DnbOb//fimvaj1uBfrv1woUL0TQt/hg7dmy/3WufQRlgGxoaGDNmDDt37owfG4g3ZyhRpkXmeVOJ7G6h7s9LaXllM23v7aDl1S3U/nkp4R1NZH53Co7RGQBY4RjKUgPbaSGEEEJ8fVYAGh6CyusgtDrxXGh1+/GGh/otxC5fvpyXX36ZpqYmmpqaWLlyZb/cZ3+DbgpBfX098+fPTwiv8OWbM2/ePAAMQxYh7aOUQrMbND+3gcjulo4NYhbB1dXE6v1kLJhE45Or0VMcIMUIhBBCiKFNWRBYBk2Pdt+u6VFwHwopR/bpdIJYLMa6des45phjSE09eLt7DroR2PPPP5/zzz8/4dj+b05GRgYZGRl4vd4B6uHgo2IWwXU1nYfX/UT3thFcW0PqUaPba8HKLlxCCCHE0KaC0Phwcm0bH25v34fWrFmDUooZM2bgdrs59dRT2b17d5/eozODLsA++OCDXH/99QnHevvmhMNhWltbEx7DXWB5ZXLtVlfjHJuN5pDNDIQQQoghT0U7ThvoSmhVe/s+VF5ezpQpU1i8eDEbNmzAbrdz1VVX9ek9OjPoAmxpaWmHY719cxYtWkR6enr8MXLkyP7s8oBTEROzKdRzQ8BqDaOCUTTJrkIIIcTQZ7b1b/seXHTRRXz66afMnj2bMWPG8Kc//Yk33nij3wcPB12A7Uxv35zbbruNlpaW+GPPnj0HuccHV2+rCSjTQjOGxLdeCCGEEN0xejmlsrfteykjIwPLsqiqqurX+wzJFNPTm+N0OklLS0t4DGe6xwFGkkOqGu0LuIQQQggx9Gl2cB2SXFvXjPb2fejGG2/kmWeeiX+9dOlSdF3v90+/h0SAHag3ZyhQURNMC9f4nKTaO8dmg1TPEkIIIYYHzQ1ZVyTXNuuK9vZ9aMaMGdx+++188MEHvPPOOyxcuJDLLrsMj8fTp/c50KAro9WZfW9OQUEBsVjsoL05Q4GyFFbUImVOMaHN9WB2k051jdQjRiY/WiuEEEKIwU3TwXMYZF7efSmtzMvb2/XxjlyXXnop5eXlLFiwAK/Xy1lnncVdd93Vp/fozJAIsAP15gwFmq4Rq2rDXpxGxhmTaH5xI8Ssjg0NjYzTJmBkuOCLDQyUaaFiFmgaKIVm02VurBBCCDHU6B7I/mF7ndfGh9urDezjmtE+8uo5DPS+HX3dZ9GiRSxatKhfrt0VTSk17D9Qbm1tJT09nZaWlmE5H9aKmFj+CLrbBgr8KyoJra/FCsbQ3TZcE3PxzCgEm47pC2PLdINSBNfUENyvnXtqPu5p+Wg6UiNWCCGEOAhCoRA7duxgzJgxuFyur3cxZYEKgYq0VxswvO1zXjV3n4+8flXdvd7e5LUhMQIruqaiJipqEq1sI7CyEhW18MwsJOuC6WhOGygFpgV2A99Hu0g9chSxGh9Nz25IqF5gtYVpe2c7vo92kXnuFGy5Keh2CbFCCCHEkKHpoHkADxgZA92bfiUBdghTMQtlWrS9vZ3Qpvr48dY3t8Gb29q/sOlknjUJPdVByhEjsfxRGv+xvvNpBrTXlG16Zh3Zlx+Kni4BVgghhBCDz+AYTxZfiTIt/MsqE8JrBzGL5ufL0Z02NF3D99GuLsNr/LpRC9/Hu7EisT7usRBCCCHE1ycBdgjTdI3Aqp4LBauoRWBlFSiIVCa3M0ZoUz2abNclhBBCiEFIAuwQFqloRQWTGyUNbqgFXUPTk/yWxyysYN/ulyyEEEII0RdkDuwQZvoj6B477mnt1QOMNCcAscYgwdXVBNfXoiLtC7UsfxTN0HsXSpMNu0IIIYQQB5EE2CHMlu0m5weHEtpcT/O/NxKr84OmYS/y4plZSMoRI2l+YSPRva1oTgMshWY3UGGzx2sbaU50lyziEkIIIYYKpSyUFUNZJioWQbM50HQDTbehDZIyWn1FAuwQZst00/j0WmJ1gS8PKkW0opWWilYcozPIWDCRpn+ux17kRZkWnhmF7Qu5euA5tBCQObBCCCHEUGCZUSIte2mrWE60rTp+3O4twFs8C0f6CHTD3q99+OlPf8r69et58cUX+/U+IAF2yFJRk6Z/rE8MrweI7Gqm7d0dpJ1Yhp7iQHfa8MwqIrS5nlitv8vn2QtScc8oRLMNr7/WhBBCiOHIMqO07VmGf++KDueibdU0lr9MyohD8Y48rN9C7Lp167jvvvtYuXJlv1z/QJJQhqhofYBota/HdqGNdRhpLnRP+w+s7jDIumAarok5oB8wwqpruCbnkvm9abKJgRBCCDEEKGURadnbaXjdn3/vCiIte1Gq+1KaX60PiquuuoobbriBsrKyPr9+ZyTADkFWOEZgeWVyjRUE11Sj7RdWdYeNtJPHknftHNJOLCPliJGknVQW/1p3SHgVQgghhgJlxWirWJ5UW1/FcpTV9zXeH3roIVatWsWYMWN46aWXiEb7v4qRBNihSIHZGk66udkSRpmJf3HpThu6y4ZnZiGpR47CM6MQ3WVDd8qsEiGEEGKoUJaZMOe1O5G2apTV80Lu3vD5fNxxxx2MGzeOiooK7rnnHo455hhCoVCf3udAEmCHKM2e/LdOs+vdbkogGxYIIYQQQ5OKRXrX3uxd+54899xz+P1+3nnnHX7+85/zxhtv0NzczBNPPNGn9zmQBNghSLPruMbnJN3eNSkXTea0CiGEEMOOZnP0rr3Ru/Y9qaioYM6cOWRlZQFgs9mYPn06O3bs6NP7HEgC7BCkGTquycmFUiPDhT0v5SD0SgghhBAHm6Yb2L0FSbV1eAvQ9L4d0Bo5ciTBYDDh2K5duxg9enSf3udAEmCHsPQzJnSsJLAfzW6QedakbtsIIYQQYujSdBve4llJtU0tnoWm9+1al9NOO43y8nIeeOABKioq+MMf/sCqVas49dRT+/Q+B5IAO0TpdgNHcTpZ50/DXpDa4byjJIPs78/AyHChGfJtFkIIIYYjTdNxpI8gZcSh3bZLGXEojvQRfb4jV1ZWFq+99hp//etfGT9+PPfeey9PP/00JSUlfXqfA8mS8yFMdxjYC71knj8NKxAlWt2G9sVWsprdQHMYskBLCCGEGOZ0w4535GE40wrxVSwnsl9VAoe3gNR+3olr7ty5fPzxx/1y7a5IgB3CrHAMFFihKJrdwFmahaZrMuIqhBBCfMPohh1n5igc6UUoy0SZETTDgaYbaLqtz0deB5oE2CHIipjE6vz4luwmsrM5ftyWm0LK7BE4x2fLTlpCCCHEN4ym6e1VBgzA7h7o7vQrCbBDjBUxCa2vpfWtbR3Oxer8tLyyGdf2HNJOHSchVgghhBDD0vAaT/4GMFtCCeFVs+sdymmFNtYTXFODivX9fsdCCCGEEANNRmCHECscw//pHnSPHff0fDzTC9DcdlAKTEVwXQ2BVVWYLWECy/bimZ4/0F0WQgghhOhzEmCHEM1uYIVNsi+bSXhrA03PlxOr9QNgpDtxH1JI9sUzaPtgJ8G1NcSaQ9hzZRMDIYQQQgwvEmCHEGVapH97HM3PlxOtbEs4Z7aE8X2wk+CaarLOm4qKWVjB6AD1VAghhBCi/8gc2KHEUrS+ua1DeN2f2Ryi6V8b8B4/Bt3dP/XehBBCCDH4KKWIxWJEIhH8fj+RSIRYLIZSaqC71udkBHaIsGImKmIS3tLQY9tYXYBYjQ/HqIwe2yqlUBEzvhBMRdv/v5bE9rPKUu0jvYEosTo/aGAv9KLZdHSn/GgJIYQQB0ssFqOhoYGtW7fS1NQUP56ZmcnYsWPJzs7GZuv7382PPfYYl19+eYfjjz76KJdddlmf328fSRlDhYLA6uqe230huK4We1Eamq3rQXYVNQnvbiGwdC+RylYA7HkpeA4bgassq0N1g/1ZUZNYfYC2d7Z3GBF2lGSQdmIZhteBZpNSXkIIIUR/isVibNmyhW3bOpbYbGpqYunSpZSVlTFu3Lg+D7EXXnghZ555Zvxrn8/HzJkzOeaYY/r0PgeSADtEqJiF1RZOur3pi3R73oqaND+3gcjuloTj0SofLS9uwp+fQtb3pnU6kqqiJtHKVpqe3QBWx48lIjubaXhiFdkXTcfIdMvOYEIIIUQ/UUrR0NDQaXjd37Zt28jKyiIvL69Pt5l3OBw4HI741/fddx9nn302paWlfXaPzkiyGCI0m47mSH40s7u2ViRG66tbOoTX/cVq/DQ9twEranZ6vvn5jZ2G131UxKTpX+XdthFCCCHE12OaJlu3bk2q7datWzHNzn+v94VQKMTvf/97brvttn67xz4SYIcITddwjstOur1rYg7YOv8LS0UsQpvqe7xGtKIVszmU+FzTIrixHhXp+R+A2RwiWutLrsNCCCGE6DXLshLmvHanqakJy+q/TY6eeuop5s6dS0lJSb/dYx8JsEOAFYmBpmHPS8WW4+mxveY0cI3PQe9k/qmKWQRWVSV978Cyyvb773t+NLnwu0+ovL7LUVwhhBBCfD3RaO9KZva2fW888MAD/PjHP+636+9PAuwQoGkaKhIjUtlK+vwJaK5upi4bGhnzJwKdf3SvTKvDqGp3zNYQHPDHmupFIFURs6uuCCGEEOJrstt7VzKzt+2TtXXrVrZu3cqJJ57YL9c/kATYIcAKm/iXV9L8zw2ENzeQfdEhuCbkwAGlrhwlGWRfdAia29Z1aNQ0NHvy3/YOlQg0MNKcST9fT3eiGX03WVwIIYQQX9J1nczMzKTaZmZmouv9E/2eeeYZTj/99H4LyAeSKgSD3L7iw/7PKnBNzMExMh3d6yDt5LGknVxGtMqHilnYcjyoqImR6QZA76IElu4wcE3MJbimJqn7uyZkozm+/GHXnTY8hxYR2lCX1PM9MwqlCoEQQgjRTwzDYOzYsSxdurTHtmPHjsUw+qe85WuvvdZpPdj+IslikFNRk9DGOnIuPxT3tHz8yyup+7/Pqb3vM5pf2YyyLByj0glurKPh8VVEdjZ3GJk9kGOEFz2JUVTN0T6XVjvgrzV7jgd7YWqPz3dNyOnVaK8QQgghekfTNLKzsykrK+u2XVlZGdnZ2X1aQmufYDDIZ599xhFHHNHn1+6KjMAOdpbCNSkX3/s7Ca6vTTgV2dZEZFsTRrqTzHOnYrWECe9swjEyDboZ9VRoZJ45iYan1kCsi9WIukbGgomdntLsBpnfnUrj4jXE6gOdtnGMTCft1HHovSj9JYQQQojes9lsjBs3jqysrIO+ExeA2+0mHE6+Vn1fkAA7BARWVHYIr/szW8I0Pbue7Etm0LZkd48jsLpNhyw3OZccQsub24hWtCactxWkkvatUmx5KV3uxqU5DbIvPoTgpnoCyyuJ1X6xlWyRl5TZxThLMrrdyUsIIYQQfcdms5GXl0d2djaWZRGNRrHb7ei6jmEY/TLyOpAkwA5ymqETWNFz2SuzOUR4W2P74i5Dx4qaaIaO1kWY1e0GWraHzHMmo0IxIpVtoMCen4qe6mjfOKGbIKxpGtgN3JNy26cZfDFVQEVMNLvR7XOFEEII0fc0TYuPsu6/O9ZwJAF2EFOWIrS1Ib5pgGNUOs5x2eguGypqEtnVQmhLQ3y3q+DaGjLOnER4cz26y459RBoK1R4oO/nLS9M0NIcNHDbcaa6v1EfN0NH2G2jVOtl6VgghhBCiL0naGMSUpYg1BHGUZJD2rVKUpQiuryVa1YbmMHAfUoD3W2PwLdlDcHU1ZksIlKLlpc0A6CkOPIcV4ZlRCHZ92H18IIQQQohvpkG5RLyhoYExY8awc+fO+LF169Yxe/ZsMjMzueWWW+LlpYY3hS0vhfRvjyewtobwtkYMrxNbpptoZRtNz6yj6Zn1eGYWkjpvFNh0rOCXO2xY/gi+93fi/3QPKmpihWLtjyS2gRVCCCHEwdOfW7wOJn2V3wbdCGx9fT3z589PCK/hcJj58+dzyimn8PTTT3Pdddfx2GOPHdR6YwPCAseYTJQvgntKHqFN9Vj+KHqKncwzJ2H6I7S9t4PGv68l++JD0LwOInvb4k/XHAbp3x6HfUQagVXV7SW2LIWR7SblsBHoKQ6pEiCEEEIMIIfDga7rVFZWkpubi8PhGLafmCqlqKurQ9O0r73hgaYG2VDmiSeeyPz587nhhhvYsWMHJSUlPP/88/zgBz+goqICj8fD6tWrufbaa/noo4+SumZrayvp6em0tLSQlpbWz6+g71hRExU2aXlpE5E9LYknNXCOyybtxDJaXt2C7raR9q1SGp9dT6zah2Y3yDp/GpG9rbS9vwPMjt9m57hs0k8b3+WmB0IIIYTof5FIhKqqKgKBzktTDieaplFcXExqasd68r3Ja4NuBPbBBx+ktLSUG264IX5s9erVzJ07F4/HA8D06dPZsGFDl9cIh8MJ9chaW1u7bDtYWTETTIuGJ1djtXZSW01BeHMDzW1hMs6aTMOTq0HXUIH2KQSpx44mWuuj7Z3tXd4jvKWBln9vJOOMiVLySgghhBggDoeDUaNGEYvFMM3hPc3Pbrf3yW5ggy7AlpaWdjjW2trKmDFj4l9rmoZhGDQ1NXW6/++iRYv45S9/2a/97HcxRduHuzoPr/uJVvkIbazHMzWfWEMAI9ONFYrhnphL/aMrerxNeHsTscYg9vyed9YSQgghRP/Y97H61/1o/ZtiUC7iOpDNZsPpTNz61OVydTnUftttt9HS0hJ/7Nmz52B0s09phkZofV1SbQOrqnBPL/jiieCamEN4VzOWP9r9E7/g/7wCKxz7ql0VQgghhDioBt0IbGeysrJYt25dwrG2trYui/Q6nc4OgXeoiTUFUdHkPkYwG4OggS3TjdkSwjEyvX1nrCRF65JvK4QQQggx0IbECOzs2bP59NNP41/v3LmTcDhMVlbWAPaq/yjTim9OkPyTwAzFMJtCoIBerGDUGJ6rHYUQQggxPA2JAHvMMcfQ0tLCE088AcDdd9/NiSee2CeTgAcjZVkY6S6SzZWa24busuH/eDcAscYA9iJv0vezF3mTDrwqZkk9WSGEEEIMqCExhcBms/Hggw9y4YUXcsstt2CaJu+///5Ad6sfaaApnKVZhLc19tjaM70AK2ai6RqawyC0uYG040sx0pyYPSwCA0iZU9xjPVgrYoJSBNdUE/6inqwt24Nn9gh0t13qyQohhBDioBl0dWC7s3fvXpYtW8a8efPIzc1N+nlDrQ6sFYoRrfWhe+w0/HU1xLrenUP32Mn5waGEK1ox3DbsBalg6KiISWRvK83PbWifUtAF97R8vN8q7TaAWhGT0IZaWt/Z3mk9WdeEHNK/PU5KcQkhhBDiK+tNXhtSAfarGnIB9osNDKK1PjRNo/nfG1GdfFyvex1knjMFI81JaHM9ra9t3e+kRubZk1FRk9Y3t2EFDqhIoGt4Di3Ce9SoboOnFTUJbaqn9dUt3fZZNkUQQgghxNcxpDcyEICCpmfXE6v34z2+lNwfHUawvI7QpnpUOIae4sA9OQ/X+GxCWxvRPXYOnDBry/WgoiaOUenk/PAwwjuaiOxoQu376P+QAtC1HkdNNU3D9/7OHrsc3tKA2RxCz035Gi9cCCGEEKJnEmAHGStmEtpYT+yL0lZt72zH/3kFnkMK8B5TgmY3UOEYoS0NhDbWkXpMCSiFGfhyrquzNJO0U8fhW7Kbllc2g67hnpyHvdCL5jDQU+0opZJaIxbe0dRx9LYL/s8rSDuxDN0pP1ZCCCGE6D+SNAYbCwIrKhMP+SL4Pt4NX1QZ2J/3hDI0p4FnagHOkRkE19eROm8kTc9tIFbti7cLrKxKeJ5rci5pJ43ttivKtIhWJr8Nb29qzwohhBBCfFVDoozWN4lm1+Ojr8mI1vgIbWrAbAljeJ2kHj2KwPK9CeG1M6ENdUT2tNDtFOhe1JJtb9+75kIIIYQQX4UE2MGmt0vqLEVkRxONT66m+cWNaAqscHL1Wf2fV3S725emazhKMpPuir0oTUKsEEIIIfqdTCEYZFTExF6QSrSbEVRbjgfPjEKcY7PQPXYco9NxlmYRWFlFw5OrybpgOpHdLe1bzHYjWtGKpnf/N4xjRBq614nVlkQ92cNHoDvkR0oIIYQQ/UvSxiBihWKEdzbhnlFI9LVOylZpkHZSWXtYXV1N4+I1WIEomtPWXov1tPHE6gME19TgmVlI29vbe7ynMi00W/chNu2UsTQ/u77bNu5DCtA9jh7vJ4QQQgjxdckUgkFFEVhZiassC/uIjlvBpp86DsPrpP7h5fg/2YPZEkZFLSxfhMDySuofWYEKx7CPSMM9KReMrj/PN7LcuKfmo9m7/xHQbDqOEWlknD0Jzd3J3zu6hmf2CNKOHyO7cQkhhBDioJAR2EFFQ4VMml/eRMaCSbS+vjW+layjJANbQWr3O3NZipbXtpB57lQsS2GkODpsJesYk0nK7BHYst1EK9sIbWnElu3GSHe1b0VrdAy0usPAOSqDvB8fTnh7I+Ht7fVk7bke3NMLQOu5nmwyLGVhqSig0DU7uiaBWAghhBAdSYAdTDSwF6QSXFdL88ubST+pjLQTSgltrsc+Io3Assput5UFQLUvzso4fUKHBVWew0aQcmghre/tILy1EawvV4zZcjykHlOCY2R6pyOp+wKqozQTx+iM9mOGjrKsrz3yGrPCaGjs9S+n2r8GhUWWayxj0o5GobDr7q91fSGEEEIMLxJgBwkrZqIZGp7DirDlp+KelEusKQSmhWtCLrrHTtPGdUldK7KzGaUAhw1oH4F1js/GM7OQhifXYPkjHZ4Tqw/Q/NwG0k4Zi2tibodQqmIWKmbhX76X4Oqa9mvoGs7STFLmFGPLTflK28hGrRBVvpUsrX2IiNkWP7695R1W1j7O1OzvMj7z29h0Z6+vLYQQQojhSQLsIKFMRWRvK47idNSWRuofW4nlaw+aRrqTzO9NQ0V7GH3dj9UWJvOcyfiX7Ca0qZ7UI0bR+sbWTsPr/lrf2IqzLAv2C7AqZhFrDND493WoUGy/myjCWxsJb23Ec2ghqceU9CrExqww1b5VfFx1T6fnTRVmdf2TmCrMxKwzZCRWiCHOtCwsS9HSFqahOYSuw4h8L4au45Q59EKIXpAAO0houoajwEvTs+uJ7k3c/UrFVKdzU7u9nk1Hd9nwHl9K2ollWIEokV3NPT9RQWB5JSlHjIyHUWVaHcPrAQIrqjAy3Xim56PZkvtFpKGxtPahHtutb3iOcRnflgArxBAWjZpU1LTxwfIKGppD8eOaBmNGpHPC3NF4XDaMXv63TgjxzST/pRgsLIXv0z0dwiuAFYiAaWHLS0nqUnqKHd1jp/Wd7aAUVihGaFN90l0JbWkAS6GUwgrH0Gw6edccTs4Vs0g5vLjzagSA/7OKpO+hlEWlfyVhs+etahUWm5teIWb1XItWCDH4RKMmm3c18a+3tyaEVwClYHtFC399cQOBUAzL6u1uLkKIbyIJsIOEpmsE19YAoKc6sBenYS9OQ/c62kdFV1fjmVGY1LXchxQQ2lRPaE0NjX9fBzYdFUludy6gfXcuTSOys5nm58up+f0n1Nz7Cc0vbcTIdJF7xSyc47M7PM/yRbrdgGF/popSHViTdJ9qg+VYqusRYCHE4BWzFG8u2dVtm1A4xkvvbyNmJj9VSgjxzSVTCAaJSEUr9sJUPIeOwFHkJdYQAMCW7SFa3UZwQy2eQwtxlmYS3t7U5XXsBamkzBpBw5OrAYjV+IhWtGJkupLui/HFzltNB2xeEKvx0/r6VgIrqsg8ZzKtpoqX+drHbAlBcXoSd1EolfwvKoX8UhNiKIrGTFaW12CpnkdWq+r8+INRHH1Qlk8IMbzJCOwgYFkWmsdO2snjCG+pp/bPS2l8ei2NT6+l9oHPCZbXkTpvFOGdzaSdOg7P7BFoBy54sOm4p+WT+b1ptL61LWEbWf9ne3CNze74nC54ZhYSXFfT5flYnZ/mFzaSdvJY0BNrdWlJbiWra3ayXWOTaguQ6SxJqAtrKYuoGSRqBjCtaNLXEUIcXJal2LanOen2m3Y0opIIu0KIbzYZgR0MTIXuNGh8cjVWINrhXGhDHeFtjWSdN43guhrshV68R44ivL0J0xdBd9vaKwcoiNb7CZXXJVzCXuDF9IXxzCjE/3n381T1NCfOcdmEtjZ22y5a1UasIYBrXPaX82sNDcfoZEZfQdcMRqcdyYrax4ipUI/tJ2bNx6a7iFkRNKDSv4K9vmWYKkqaYwTjMk7F0O2y0EuIQUcj2lP96v1EYxZKtS/uEkKIrkiAHQQ0oOmfGzqG1/2osEnzC+VkXzoD3+d70VwGRpYbR1lW+yoIpQisq8Fw2Ts81zkmE/+nFaQePRqzLdwh4O6jex1kfXcK4e1NOEakEe5h4VdwbQ2uSbnxAOuakJP8i/7C1OxzWVX/127blKQdg9NII2aFqAtu4pOqP3RY/LW+4Z+MSTuOWfk/kJqxQgwqirQUJ82tyS3CzExzouuSXoUQ3ZMpBINAtNaf8JF/V8zWMJE9LaQcPgL/B7toeGwlbW9vQ0VM6h9bifJFsYIdQ7DmMIg1BWl6Zj3eo0eTcfZkHGMy4x//GxkuUo8tIefSmQTWVBPZ3pTUdAOzNYTuaQ/MRpabtBPL0JOcQgBg012MyzyZadnnoXXxo1iSdgyz83+Ejo2G0Dber7ir08oFCovtre/wadWfpFqBEIOIw24wc2JeUm1tNp3xJVn93CMhxHAgI7ADzArF4tUHkhHaVI99ZHp8tX9wTQ22LDcps0bgHJ1ByxtbOz5J09DddsJ726h7ZAXuCTmkHjkK+1mTQNOwAlGC62tp+NsqzJYwKYcXo8I9r/jXbAYqZuGelo/3+DHx7WZ7w6a7mJA5n3EZp7Kp+RXqAuUoLDKdo5mYdQYOw4tNdxKzwiytfrDHxVx7fJ8yOXIWWa7SXvdFCNH3NE1jdFEaGWk9j8JOH597kHolhBjqJMAOAlYSYTHeNhTjwA/X/Msqybn8UMy2cMc6soaGnmLDPTWf8NZGiFkE19cSXF/bfl4DDlgv4ZqcS9vb23vsi2tcNvYiL/bC1F6NvB7IbrgAF5OzzsTKPL29W5qBXf+yckJLeA9t0cqkrlfe+AKH51+F3fB85T7FYjE0TaO1tZVYLIbL5cLj8aBpGrouH1wI0Ru6rvG9Uyfy9KsbaWnrPMSOH53JkTOLsCe5EYoQ4ptNAuwgYKQ4km6rpzgOzJtYvgixhgDh7YkLrzS7TvrpE4lW+3GOSsdId2K2HPDL44CLOUano9l0Intauu2HZtdxTc3r1daxPWmfu9r5/NWawLqkr9MQ3PK1VoDEYjG2bNnC7t27iUa/nJKRkpLCuHHjKCgowGaTfzpCJEvXNdxOG5eeMZnybQ2s3FhLY0sIXdMYVZjGYVPzKchJkfAqhEia/BYeaIaGe1o+gZVVSTX3HFKApmlkXzIDzWmgIibh7U3tC8C0L3bhcttxTczBPb2A8LZGWt/chueQAjLOnkzTM+uw/J0vFrNle8g4Y1L77lt2o31Dg85okD5/wld9xV9R8mV1FAo6jFMnJxaL8dlnn9HU1LHWrt/vZ9WqVYwdO5axY8dKiBWiF3RdQ9cNpozLZVJpNoZNb6+cErNwJlniTwgh9pHfwAMs1hjElu7CXuQlWtnWbVtbbgq2vBRirWHa3t6GFYiiu224JubiGJWOfUQanhlFgAJDJ7SuFt9ne8BSBFZWYWS6yP7+TPxL9xJcW4MKtU9d0L0OPDMK8RxahGbXMWwa2ZfOoPWtrUR2JY7E2vJTSDu+FFtBap+OvvYk1z0p6baZrjG92iRhn30jr52F1/1t3bqV/Px8MjMze30PIb7pDF3D0L/4b4eGhFchxFciAXYAWaaF4XXS8tZWMuZPpPGZdZhNnVcjMNKdZJ4zGTTwf7ArHnbNZohW+fB9vJuMBRMx2yK0vrYFW14KmedMxnNoIcpUaFp7tS3NppF65Ci8R41un3uraeh2HTQNzfbF3E6bgZHpImPBJFTEJFLZCpbCnpeK7nWi2TS0gzwPNNNVSoo9D3+0tse2k7POxPEV5r9qmsbu3buTart161ZmzJiB3d6xbJkQQggh+pcE2IFkWgRXVREur6dN18m6YBrBNdUEVldjtUWA9jmv7un5eGYWotk0MBXRWl+HS6mISdO/ysm5bCZ5NxyBCptohoaKWqBraHYj/qG69kX2NGxdz73VNA3NaQOnDfeEgV8ZrGkas/J+wAd7/4fuphMUemaQ4Rz9le7R2tqaMOe1O7W1tTKFQAghhBgg8ht4AGmaRuCLElqh9bVEK9vwzCwk5/szUTELVPtiqeDGOpqeWYf3hFJs+SlYXZSi0XQNyx9Bd9sJrq8hsrsFTIWR5SZl9gj0FDsqpghtqkdFYtiyPThLMlCKgzod4KswNDv5nikcWXgDn1Xf3+nuXcWph3NE4cKvvJFBLJZ8NQilFJZlYRiD+30TQgghhiMJsAPJpmPtV1LGbArS9s522t7bEd8gwApGwWwfcbR8ESxP5x9Za3adrPOmEq320fjMuvhzANjTQnB1Nc6xWaSdPLY9LFe1T0HQXDZSZo/AM6to0IdYm+6iKPVQzhr7EDtbP6LStxxLxUh1FDIx8zRctnRs+5Xe6i2XK/nn2mw2NNnrUgghhBgQEmAHiFIKLNW+2j9ywGp/S2H5Ih2eozkMQlsbOxwHSD26hFhDgNa3tnV5z/DWRlrMLWScMZG6h5aBpVChGL4Pd2G2hPAeX4o+yBdU7AuopenHMcp7BAC6Znyt4LqPx+MhJSUFv9/fY9vi4mIsy5KasEIIIcQAkN++A0QphRWI4hyb5LaJNh3HqAxC6zvu2qXZDdyTc2n7YFePl4nsaCLWFMQ1LjvheHBNDbH6QHJ9GQR0zYbDSMFhpPRJeIX2KR3jxo3r+d66TllZmcyBFUIIIQaIBNiBErEIrqvBM7MwqebuCTlgKczGjnM/XRNyiOxuwfJ3HLXtTGBVFe5p+R2O+5dW9GpXsOFG13UKCgooKyvrts3s2bNxOJLffEIIIYQQfatPA2yyJYgEaIaGb8luNE0j5YiR3bY1st14v1WKFep8hbyR7iRa1/PH3vvE6vzYcjwYGYkjl5EdTV+W0vqGstlsjBs3jiOPPJL8/Pz4PFebzUZJSQnHH388WVlZsnhLCCGEGEBJfwa6adMmrrzySpYtW8bYsWO59tpr+eEPfxj/Re73+xkzZgym2cXuTSKBilpgQdO/NpB57lSMdBf+zyoS6sBqdh3XpFxSjxqNFYwQ7WT09cvGvbm7BppG1oXTidX68X28i2iVD2Wqr7UF63Bhs9nIzMxkxowZ2Gw2LMtC0zQsy5JpA0IIIcQgkPRw22WXXUZRURGvvvoq1157Lb/73e+YPXs2mzZtirdRKvntPr/JLMuCL0Y6LX+UxsVrsHwRsi6YRtYF00g/bTwZCyaS+6PZOEuzaH6+HNBwjkrDNTGnQ1iNNQZwjEhP+v6OIi/Rqjbq/ryUUHkdGWdNxlmaiZHhai/fJQCw2+1omoZhGOi6LuFVCCGEGCQ0lWTqtNvtVFZWkpvbXtQ+Go1y++238+CDD/LAAw8wf/580tLSBuUIbGtrK+np6bS0tJCWljbQ3cGKxFAxRcvLm4jsbP7yhK7hGJWOkeXB8DowW8OYDQFMf4TsS2ZghWJYLSGMNBeRylZUxAIdnCPT0dx2Gh5fidnczSjtF7IvPoS2j3cT2dG+ZaotP5XMcyYT3tWEe0IumjH8phFEzSAKCw0N+1fYpUsIIYQQ/as3eS3pIaVRo0bxwQcfcM455wDtgfbXv/41J510EhdffDHvvffe1+r0N4qmYQUipMwqSgiw9oJUPDMKcYxII1LRgpHqwJiUiy3bve9pWOEYNqeBLcuNiloYXgdKQXRPC95vldL8rw3dbVSFe0oemsOIh1eAWI2PwKoqUg4bMazCq6UsLBWlObyLrc1vEDJbcegplKZ/ixz3BHQMdF3msgohhBBDTdIB9p577uEHP/gBDQ0N/OhHP4ofP+mkk/jkk084/fTT+6WDw5KlaH1rK2nHl5JyxEj8n+zBNTkX77Fj8H20i+aXNsF+H+Xbsj2kzC3GOSYTzWFQ99AyVPDLagH2Qi8pc4uxF6SSsWASrW9uxfIfsOBL1/DMKCRlzggan17XoUvB1TWkzinut5d8sFnKJGy28u6eX9ESSVxcuKvtI1Lt+RxXfAceezaG1vnmEEIIIYQYnJKeQgCwbds2Nm7cyGmnndbhXCgU4vXXX2fBggV92sG+MNimEMRaQjQuXkPqnGJck/OI1fsxMtw0Ll6bsIjrQKnHjMZzSAENT63BbOjYLu2kMpzjstHsBpFdzYS3N8a3knVPzW/f6OCNrV1OM8i5cha2THefvc6BFDEDvLrzJgKx+i7bOAwv3ym5B7ct4+B1TAghhBCd6k1e69XnxcXFxei6zpIlS+LH/vznP/Pqq6+ilBqU4XWwsSwL0xcm++IZoGkEN9RipLuxglG8x5bgmpQLRueVAHwf7MIKxsi+8BBS5hSjuRIH0Fvf3IaKmKioiZZqJ/XI0ThGpYOCxr+vpemZdd3OkVXm8FjAFbPClDc+3214BYiYbaypX0zU7PqPBiGEEEIMPkkH2OXLl1NaWso111yTEGCfeOIJrrjiCkaPHs2KFSv6pZP7LFy4EE3T4o+xY8f26/36hQJ7TgrRWh+uCTlgQcsrm2l9fSuhjXW4p+aRe9VsnAfslLWP//MKotVtGBkuci6dgZHl7nA+1hDAagqhOQ38a2rwfbQLs7GHkKaBkersq1fZZ6JmgFCshZ0tH7K1+S1qA+XErAim1XlNXAANjW0tbyd1/V2tH8drvQohhBBiaEh6DuxVV13FHXfcwdVXX51w/OOPPwbgV7/6FVdddRVLly7t2x7uZ/ny5bz88svMmzcPYGgWkzcVpi8MUYu6Py9trwf7hWhlG6GN9e1VAc6aRJuhEdqYOIoY3tqI9+gSmv6xHteUPLK+O4WGv63GCrQHutCmerzHj6H295/iLMsi66xJ7VMOegiwztKsXtaS7V+mFSNi+fi8+gGq/CtRfPk+eWzZTMn+LiVpR3W6jWzUChA2W5O7jwrjj9aT7hw+839jsRiaphEIBLAsC7fbjaZp2O0y11cIIcTwkHSA3bhxIyeffHKX5y+66CIWLVrUJ53qTCwWY926dRxzzDGkpqb22336nQ5WW4TmFzd2WS0gVuOj6dn1ZJ0/jcieloQFWVbERLO3D5yH1te2Vy44tAjfR7sAUBETzd4e7MPbGvEt2Y33uDE0P7ehmz5ppB41Ct05eOqcRiwfr+28hZDZ3OFcINbA0po/E4w1MSlrfichtndJfLiMwCqliMVibNmyhT179hCNfvlzk5eXx8SJE0lJSRmaf/gJIYQQ+0l6CsGpp57KddddR21tbYdzPp+P22+/neOOO64v+5ZgzZo1KKWYMWMGbrebU089tcuta8PhMK2trQmPQSOm8H24q9tSVwCx+gChzfW4p+UnHDe8Tqzgl8EksKIS9/T8+LxZPcWBCn1ZoSCwpgZHcVqHqQZfXlAj48xJGINo8VbUDPBZ9X2dhtf9rWt4hmCsqcNxu+7GZWQkdS+b5sJjy/kKvRx8YrEYH330Edu3b08IrwC1tbV89NFHNDY2EovFuriCEEIIMTQkHWAffvhhHA4HRUVFjB8/nqOPPprjjjuOQw45hNzcXDZv3sxf/vKXfutoeXk5U6ZMYfHixWzYsAG73c5VV13VadtFixaRnp4ef4wcObLf+tUbyrSw/BGi1b6k2gdWVXcIsO5peYQ2fTmtwGwKYbWGsRe0j0q7J+cR2tLw5RNiFqHNDWR/fwapx4zGyHKjuWwYGS5S5o0k7+rDcYxKR7cPnlE5U0Wo8q9Kqu2GxheIWonTIxSKsRknJfX80WlHJ0xPGKqi0SirVq3C7/d32cayLJYtWyY75gkhhBjykv7MOD09nfvuu485c+YwYsQIqqqqiEajZGRkMHPmzPi81P5y0UUXcdFFF8W//tOf/kRpaSmtra0dSi3cdttt3HjjjfGvW1tbB0WIVQqitV0HjAPF6vwY3i8XVmluG+6p+TQ+tSahnRWKoTlsaHYdz4wCmp4vTzhvtoTQDJ2UWSPwzCxC0zWUpdA04tMNBoqlTCwVBTQMzYGmaVT6V9DjEPUXqnwr0PIuTzhm051MzDydna0f4IvWdPlcl5HB9JzzseuDZ/T5q7Isi5qarl/rPqZpsnv3bkpKSmQqgRBCiCEr6QD7xhtvcO6558Z/8W3evJn77ruPSy+9tD/716WMjAwsy6KqqqpDgHU6nTidg29FPaguS2R1ar+5mZrLRubZkwmurelQCkt327DlePAeM5pYUzBhigGA5jRQpkK36fHZoV31woqaoBSaoffrrlxRMwSaYkfL+zSHd6FpOiNSZlHgmd5+LkkxFUHr5NUYuouTRt3F+3vvojG0rcP5NEcxxxX/DLs+PLaV3bt3b6/ajho1SgKsEEKIISvpAHvLLbfwu9/9jiuvvBKAN998k+9973sHLcDeeOONzJ07l/POOw+ApUuXouv6oBhZTZqlcBQkvwDNMSKNWGOQlCNG4jmkgOCGOgIrKjHSnFhfzHNNPaYEI9uDe1IuZlMIdI2c788kUtGK/7MKolVtuMqyUTGTSI0PW5Yb84tpDJoG9hFp6G476Bqxah+hTfXtW9RmuvAcUgC61ueLu/bVad3Y9CKmisSPb21+g3HppzAi9bCkr5Vqz8NSJgdGMV3TcRqpnDDyv2iLVLO15U1CsRYcRgpl6SeQ4RyNrtnQtaEf4pRSRCKRnht+IRqNDpuFa0IIIb6Zkk4m5eXlzJ8/P/71SSedRDAYpKqqisLCwn7p3P5mzJjB7bffTkFBAbFYjIULF3LZZZfh8QyhETQFVjCGoySDyM7mHpt7Zo/AyHRhpDsJltfiGp+De2oeKmqhe2yomCKyo4mGx1cl7OCl2XVck3LJOGsSgTXVqKiJarNQMYvGZ9YRO2Aag73Ii/f4MZgtIQIrq+LHfR/twj0lj7QTy/psqkHUCrGmbjGbm1/p9PzOtg+Zlns+LiOjx0VcAOMzT8XQHJ2e0zQdm+Yi01XCTMclWMpC03TsnZTe6ollWSil0HV90IU/TdNwu5OfBuF0OmUerBBCiCEt6QBrmmaHsOh2uw/aiuZLL72U8vJyFixYgNfr5ayzzuKuu+46KPfuC1bMJLi2hmiND+9xY/Av3Yst242m65htYUIb6xLKZTlKMnCMSsdsC+Mal0N4WyMtL28iWuUDDbIvPoTQtkb8S/Z0uJeKWgTX1BDZ20r2hYcQbQig/BGa/9156a5oZRuNf19H1jlTSJk38strKgiuq8UKRMk4Y2KfhNiw2dpleIX2Gq6725YwKesMVtY90e21PLYcRnmPRNd77ldn9WJ7YpomAE1NTdTW1qKUIi0tjaKiIpRS2Gw9//PZVw0gEAigaVr831Ayz+2NoqIi1q1bl1QwlekDQgghhjpNJTkUo+s66enpCaNPzc3NpKWloetfzpVsbGzs+15+Tb3ZW7e/mKEobW9sa1/9f3gxsYYA4W2NKFNhy3LjGp9NeFczbe9uxzEqg7RvlWJFTXSXndbXtyRsaOAsyyJlbjGNT67p5o7t3FPz8B43hroHl6EiZrdtda+DnEtnUvdg4gYLAJnfm4pzVMZXeu37RM0Qq+r/xtbm17tt5zTSOGnUf7O1+Q02Nr3YaRuPLYcTR/1/uG1Z/TINIBaL0dzczOrVqwkGE6scGIbBuHHjKCkp6TKImqZJOBymvLyc6urqeLA0DIOioiImTpyI3W5P+Lfzdfu7bt06Kioqum3ncDg44YQTJMAKIYQYdHqT15IeBnr33Xe/dse+6dwzCtBsBpFaH/ZsDymHF6MshdkcpPmVTTiK0sm58jBUKIYVMdGdBoHllR124/LMKEj4qL87wY31eI8vRXfbMHsIsFZbhMjeVlwTcwmuTVzR7v98L/b81K81H1ZhUR/c2GO7sNnKO3v+ixNH/Yox6cdR3vgClf6VWFaUVEc+4zJOpSTt6H6bw2pZFs3NzXz22WedjmiapsnGjRuJxWKMHTu2Q4i1LAufz8eSJUvio7j7P3fPnj3U1tZy1FFH4XQ6+yTE2mw2pk6dSiAQ6PKPSLvdzty5cwfdFAghhBCit5JOI8cee2x/9mPY0wwdW7YH3WEQ3FBH8we7sPwRNKcN14QcMk6biFIKsyGIf0UlZlOQzHOmEFhR2eFa9uJ0ml/clNyNYxbhnU3Yi9IwW+p6bB7e3oi9yNshwEb3tvZJVQKlkqu5Gog1sKX5NabnXMBh+VeiY4CmY6kohubo18VXSilWr17d48fxW7du7XQUVinFZ5991iG87i8cDrN06VLmzZvXZ6OwNpuNOXPmUFFRwY4dO/D5fPHjxcXFjBs3DpvN1mf3E0IIIQbK4Nk7dLhTCisQpeGxlViB/ctchfHV+fF9spuMMyZiy/EQ2lCLc1w2kb2tB7Rtp9l0VLT70dSEW++3/WyPbWMWmq2Ttkr1dofWDjQgwzmalkjHebudyXaNQ9cMdO3LBUqG1v8/sk1NTR2mDXRl27ZtTJgwIR5iLcuitrY2qaoAra2tBAKBPp3WYhgGI0eOpLi4OL7wzDCMpOfsCiGEEEOBDMUcBJZpQUzR+PRalGnhmVVE+nfGk376BLzHjcGWlwKmovmFjVjBGN6Tx6I7bVj+zkOQFYyie5Ovc2uku7CCyS22M9JcnYZmW14KKvb1dqyyGx4mZZ2RVFuH4aUoZebXut9XYVkWdXU9j1Tv09DQkDBSa5pmj/NQ97dnz55uR2q/Cl3XMQwDu92Ow+HAMAwJr0IIIYYVCbAHQ8zCt7SC1HmjyP3RbOz5qYR3NRPe2oAVjpF55iSyLpyOkeHC9+EuXGOzURET3dV56AhtqsczNb/TcwfSvQ7sRV4iO5uSau+elk9wQ8cAlzJrRJ9UIfA6iihKObTHdofkXIRKcjeuvmZZyQf1zqYZ7Ks8kIxoNColrYQQQohekgB7EGg2HefoDIw0J3V/WUbLK5sJra8ltLEe/yd7qHtoGcG1NWSdPw0rEAGlUIaGY2Q6mqNjaAyuqsZ9SD6as+dAmXLYCLAU9gJvj21dk3JRwSixGl/CcVt+Cs7STDT96y/+selOjiz6CSNSZ3d6XsNgZu73KUk7Ept+8HdT03WdjIyMpNt7vR3f197sAud0OmVRlRBCCNFL8rniQaBMC2Uqml8oT6jDqtn19p2wnDbMtjBt7+0k48zJmM0hbKlOwjubcU/LJ7A8cSFXrCFAqLyOzLMn0/TcBlS484+gPTMLcY7NIlzZQsaCiTT9c317HdlOOEoy8B4/hqZn1ycct4/wknnWZOiD8LqPTXcxr/A6ArFGyhtfoDWyFx2DgpTpjMs4BQ3jK9Vt7SsFBQXYbLakahyXlZVht9vjX9vtdkaPHk1VVXJVIkaPHi0lrYQQQohekgB7MGgabe9uj4dXPdVByuwRuCfnEa33g6UwMtzoTgMFGHYXRo4b/+cVZH53CtFqH9G9rQmXbHtvJ97jxpBz+aEEVlURXFvTvhGCruEszcQzsxDD66TpmXU4xmSiWRqZZ08hvLOJwKpqYrU+QMNe5CVldhH24nTC2xvR3XbsI9Iw0p14phVgZLvBrvdJBYL92XQXaY4iDs29HEV7ANc1Oza98121DialFOPHj2fDhg3dtsvNzSUlJaXD8czMTFJSUvD7/Z0860s5OTk4HAP/eoUQQoihJumNDIaygdzIwLIsYtW++KYDthwPmd+dQrC8juCGWtKOG4Mt001gdTXhXc1gKoxMF56ZhdiyPPiX7SVl9giCq6sJrK7G8n25sMtenIb3uDHoHju6y9ZePUDTiFa2ElhZRWhLA5iqPcxmub/YGjYfz8wCjHRXe2UE06IuspGa4DpyXRPI0kuxGx6sQJRYTQDH6HR0p4Fm+/qjhEqpIfNxeSwWY/v27WzevLnT87m5ucyaNavTxVGWZREOh/n4448JhUKdPt/r9TJv3ryE0VshhBDim6w3eU0CbD+zoiaBZXvxfbQb57hs0k4pQ/mjmCETW7qT4IZafB/u6nSLV/uINDIWTKTtg53Y81NxT87F8kcx/REMrxPNYRBrDdG0eC2YCgyt/X8PkD5/ApE9LQRXVQPtUxfyFs5lXcM/KG/+N6ZKrHbgNNI4PP9q8rQJ+N6qIOPb477SBgaWMrFUFH+0nr2+ZZgqSqazhMKUQ1CoAZnj2huxWAzTNNm+fTsNDQ1YloXX66WsrAyPx9Ptyn7LsuLP3b17N+FwGACPx0NJSUl8O9ehEuiFEEKI/iYB9gADGWCVaRHcUItzTCZoGpquYbaG0RwGeoqD4NoaAisqMZs7H6mzF3nJOHMSdQ8uI/208dhzPEQbgxipDgyvk7oHl0EX5a2MLDcps0fgmpCDilqoUIzQtgaC62vJvvQQ/r7tgm56rnF0/i3kRMbhzE7r9RQC04rgi9bySdUfaQpvTzjnNNKYknUOZRnfGtC5rsmKxWIJlQJ6M2q6r0SWaZpomoau6/H/FUIIIcSXJMAeYEBHYGMmmAqzOYTv0z2EtzaC1f6W614HnukFuA8poOXVLcTqA+huGypiYraE4qOyGWdPwkhtH3H1L92LZmi4JuZiy/Hg+2QPgaV7E2+qa6SfMhZHSQbBtTWEyuuxQjH0FDvuSbm4p+UTUQH+teeHdDr0+wW3kcnppX/q9bxUS5m0Rap4Y9dtxFTnwRxgavZ3mZi1APsQCLFCCCGE6F8SYA8wUAFWKYWKmET2tND8742dfrwPYC9IJfO8qShLYbVF4qWzgqurCKypwVHkxfutUuofXZFwDSPTRcaCSZhNQZpf2Nh+UIOMMyYC0Pzy5k5HZzWnQfp3J7LXuYLP6x/o9jUcVXQL+Z6pGLodQ0tu5DFmhXhr9/+jKbyjx7ZnlN5Hij03qesKIYQQYvjqTV6TzzH7kTItsBTNL27qMrwCRKt9tL29nVi1j4bHV1L/0DKa/12OkeUh59IZ7du76h3nt5pNIRqfXIMtx0P2FYfimVlI+rfHY6S72u/ZxdQCFTZpeWYjxbbDyPdM7fY11AU3sKv1Q4LRRmJWhJgVpi1SRWNoO4FoAzErhKUS7+OP1iUVXgE2Nv6bqNn1KK0QQgghxIEkwPanmCKwsqrLILm/4MY6bHkpGBntH6fHavy0vraFto93k/6d8V3u1qSiJq1vbsPw2LGXZuAYk4nv493xaQpdUVGT0Ge1TEg9rft2ysLC5N2KX6FUjNd2/Scv7biO13f9Jy9s/zFv7/kvKn3LiVlfhtAK39IeX+8+VYE1dDeNQQghhBDiQBJg+5MGwfKO27J2ylSEtza0L/baT2h9LaEtDahY1yEvsrsFKxjDnpsKShHe3pjULUMb6shPnYZNd3fZJsNZgj9aiy9aTW1wI9musoTzjaFtfFj5azY2vkjUCqGUwlTJb6VqqRjIQnwhhBBC9IIE2P6ka6hQz7s57WMFY51uHRtYXomRYm8vk9WFSEUrmk3HbAknPaCpohZmKITLSO/0vF33UOw9nJ2tHwKwp20JhSkzOm27tuEZmkM7Ucoi01mSXAeANHsRSvU8Qi2EEEIIsY8E2H5iWe3zX3V38iWXdLcdFem4LazZHCLWFMJe4O3mhopYvR/N0btvqWYY7aOgnZiafS6VvuWEzfZdwCKmv9vR2g2Nz9MU3kmBZxoOPTWp+0/IOg277ulVn4UQQgjxzSYBtr8oQNNwTUlyhb1NxzUuu8uP/61gFN3Z9W5YtlwPscYgtnQXuie50GxkuVGGRTCWeE8Ng+k5F1CYMoMVtY/FjzttaUTNrrdHrfKvJN05ggrfMiZnn9nj/TOdY8hzT5Zi/kIIIYToFQmw/UUpYrqFY3o+mr3nt9k9OZdIVVv7FIBO6G4bVrjj6CzQvvgrxYEKxlCWwj09P6kuphxWBDqUZZxIrnsy+Z6pTMs5n7PH/oVxGadi092MzzwVt9E+L7ck7ZhuF2gpLKJWkA2Nz1OcOodJWWd02TbTWcK3Rv4/jF7WmE24n0w9EEIIIb6Rer8/qEiKpRTLGyuwojGmnzUB/3Ndl7Wyj0gj9ajRND27vtPzRoYLI91FtLqt40kNvEePJrCqGs+hhViRGClzRhLe1kisLtBl/xwj03FOyMTCYoz3GLzZhYRNHw2hrby5++e0RipId4xkbMbJfLvkt6xveB6vvZC9vmVdXlPXbNh1D/5oDW/v+QVHF91CWfpJbGx6kRr/WiwVw+soZELm6eR7pnQIr1EzgPpiAq9Nd6NricHfUhaWiuCPNlDh+wxTRchwjqYoZRZgDYldvYQQQgjx9UmA7ScxXfHW3nIq/S1cMXYuky6dRvTTSkKb6uP1XI0MF55DCnBNyaPl5U3Eajv/eN4zs5DgutoOdWA1u07aKeNA0zBbQyhTYUVi6B47medOpe2d7YQ2NySW1DI03FPySD12JE1bX8ORXkxa0STe2fPLDrVbWyJ7WF77MNta3uaEkb9ga/NbKDofBQYYmTqXumA5pooQjDXyxu7bmJl7KdOyv8e07PPQvgikBo54eG2vWhChObyLLc2vE4w1YdfdlKQdQ1HKTNA0DM1OzIoQiNbzSdXvaTxga1q7nsLkrDMZn/ltbLozuW+QEEIIIYYsCbD9wLIsTEtR4W8G4KGtnzAtq4iTD59AyYmlRALh9u1g3U6itX4a/rYaq7XzqQOuSbm4pxcQrfPjmpSL2RJCs+k4y7JwT84jtK0R3yd7yDxzEtg0gv5yIjUG7uzxeI8fg/f4UkKb61GhGHqaE9e4LFAQDuxFWSauwol8VPnbbjceaA7v5IO9v2FuwbWsqV+MopPdvdCZkHka6xv+mXC8IbQVty2bJVX/G29XmDKD2fk/wmGkEbOCvFdxJ03hnQnPq/B9jsvI4Nji20hzjCAQa+D13T8lZgU73Dtq+Vld/yShWDPTc8+XkVghhBBimJMA2w+ilknYSqyFuraxkrWNlaTanaTZXcQsi5AZ5fqJx5J+ainRzyqJ7GqJt7flpuCZXYRrbBboYM/1oB8xEhUxURGTaGUbTS9uxDU+k8zvTiZS40M5m2jbuQSA1p0f48ouJb3kOGx5HuwFXqLBBvx1a/BVrMQ78jCyps6nLVJFTWBdj6+pLriBkNlCYcoMKv0rEs5p6MzO/xERy9fhnNNII2p9ObKssKj0r+CDyt9wwshf8Obu2/FFqzu9Z8hs5u3dv+DUkl+zs+WDTsPr/jY1v8zYzJNIc4zo8fUIIYQQYuiSANsfNA2P4cCuG0StxI/cfdEwvuiXo613r3+LI/LG8L0FM9FNhRVqrwUbtYFmaGiGDpoGmgJXGFu6FxW1sBek4p6ZQ6BmHWbUgy3LRt3at7G5M/AUTMWZXoxm2LFiPuyFacQCDUR9Neg2F8qMYM8qpj64mT2+z5J+WTta3mVcxinxkKpjo9h7OBMyTyNqBflo7287jM6WpB1DRdtnzMr7AU7DS8yKUBtcT6ZzDFuaXu8yvO4TUyFW1j3BpKwFrG/8Z7dtAcobXuDQvMuxG12X+xJCCCHE0CYBth84dIO1jZUcljOKT2q7/mge2kdr60I+onoUl25iGU7ertrMv/esZf7oqRxpmPh3foxmOHDnjielcBqxUAtRXy3u7LH4qlaRUnwIZthH+tjjcKYVEqjZQNOWt1CxMLrDgydvEu7c9u1oY4F6XFkl+K0molaAQKyhx9eja3ZGeY+gJO0Y0pwjOHvsIyilMDQbDaFtlDf+m72+pR3C67j0k0lzFDEm/Th2tL5LfXAzNt3FKO+R5Hum8MrOG5N6Pyt9K5iVdwVpjhG0RvZ227Y6sLY98CfJNNv/wNB1fdCV84rFYmiahqZpWJaFruvouhQOEUIIISTA9gMFfFC9hbNKZrCsfneHUdj9aWjMHz0NV3QryjmWe9a/y4629lBZG/RBTkH7Nc0Igep1BGo2kDnuROwpuSgzgnf0XMJNuzFc6ei6jZrlf4X97meG22hpq6Ft92dkT1mA4fTiLJzI6pYXKUqdhb2bjQkARnuP4tC8y2gIbWNT8ysEow3YdDejvfMo9h5OS2QPe33LOoTX0rRvcUjuRSyp+gOV/uUJ5/a0fcr80j/ij9Ym+X5atIR3k2rP7zHAWiqG1sPetPtCa1NTEzU1NViWRVpaGiNGtE89sNkG9p9FLBYjHA6zbds2ampqME0Tl8vF6NGjGTlypARZIYQQ33gSYPuYaZkEzSgxy6LC38SPJh7Fgxs/iofYDIebYwvGMC0zC4duw22z47RnotX8L+TewLGFY5k/chIFLi9Ow45TAzO7jFDjDlAWKIumLW+RM/0cIi2VuPMmEKjegG730Ljx1fY2nbCiQRrWPU/uoRdi0zSivhC1gfWMSJ3NztYPOn1OadrxTMn5Lu9W/H80h3cnnKsOrGZF3eMcVXQTRxXdxIeVvwEUblsW4zNOY3zm/9/efcfJeZb3/v88ZerulO2976r34iLJvWJsbMoBHyC0AM7BP0JCDiEkpHAgmDRCTjgYHEKAhBKTYJpx77Ysq8vqWmn7anuZmZ2d9pTfH6sda7VtJK20Wul6v177smfmnmfuebTSfvee67nuO3i+/UsMxBsnHXeqi8Bmp2T0PL+zFMue/hcGwzAIhULs3buXWGxiTe2hQ4eora2lrq5u3kKsYRi0trZy+PDhCfePjIxw8OBBGhsb2bRpE16vV0KsEEKIK5YE2Dlm2jY6CjeUNPDIkVf5YP1VfHn93bzW00Sx28XqvFKIPIEj9AzYo6AXYAfeB0VfwLYdXFOYj22ZKIpKMnySeKSHrJKV+Gs2M3TkKVIjPWBbRDv34i1ahqJquHMqCbe+Pm14HWcZcUY6duPOq+Xa/E9yMv4mRd7lePW8SaUEWXoBqws+OONFVilrlJc6H+K2yq/ynrrvY2GiqS4iyS5aw69OGV7HnhfDtFP4nKVEkidnPacqOjnuGiLJmetlARbn3I1jmi4EpmkSDofZtm0btm1P+XhjYyOGYbB48eKLHmIty6K3t3dSeD1dMplk69at3HTTTRJghRBCXLHkJ+AcUxUFO5WgIVDI51Zs4eqCcrJ1i7eX17I+ewhHyx04+v4aYtshfgBGXkDp/D1o/xCKFSLU9DLd2x6h+43vEutrxJ1TjW0ahJpeJXfpXTiyCwGIDZzAkV2AomioDg+J4faM5jfaexiHN4+Bvf9NIVWkUiPcWvllbiz/IptK/oAq3xZURac+eBvN4RdnvcjKsg329v0QFAWX7kNTdLyOXBqHn5rhWTYnhp+jIXB7RnMu912FYcVnnUvAWUlJ1up0v9mp7Nu3b8rwerrm5maSyWRGc5tLtm1z9OjRWcclk0laW1vTpRBCCCHElUYC7BxT7RRuxcRLhJrkf6I334pj4BsoyWMonQ+CNfVmBSSbUTo/RqBqHZrbj20ZjPYepm/fo1ipUbKKlxE68RI5i24bG29b2GYKy0hgJkYynp9tJLCtFIqqETr8NB41QN/oMY4N/ZaT0d3UBG7gHbUPUxe8jePDz2R0zJ7RA6SssV2/FEXFoXpn7CsLcCL0LNX+68hxVc84zqlms7bgQ3j1XFbl/89pxwVdldxS+SVUxTHtmHA4TDQ6zfk/c34nTmAYRkZj50o0Gs14fq2trRd4NkIIIcSlSwLsHFOwgE7U1ntRhv8DrAgE3wf93wJmCUSpTgg9Rlbx0tPutBk+/gKq5gRFxTKTuIIVY6+l6lhmCnuW0oFJc1Q0bNvEtlKMnNxLoXsxG4sfQFfdvNjx17zU8RC66p51xfN0oWTHWc1h1Bhge893uKH8Tyn2rppyjM9Ryu1VD+HS/Giqk0U5d3Fv7bdZnHM3fmcZWY5Cir2ruaHsT7m98iGcata0nQRs26anpyfj+Q0ODs66UjvXMg2vALFYTEoIhBBCXLGkBnYOWZaBQgql83+9tdLqWgqKE2I7MjqGEn4Ub8V/Em45vT+rzcjJvWSVrGK0+yCewiVYRuLUVxxHVh6K5sA2U9Med5yelY9lpbCNsV60sd4jZFWs5am2P2Vz6R/i0XI4OPhzOMvwdvqV/5Zt4HeWE54l1HaMbMewEmwu/SxJc+TUVrKD6KqH2sCNBF3VqOioqgaAQ3XjUN2syn8fK/P+BygKtm3jUD0ZtcA6m0B6scMrgKZpGY+91Fp+CSGEEBeTLOHMJTsJw/81tuo6zlECieOZHyPViaI4UNSJH4XHB1twZOVhJqNozmyySlYS7T5ApPV1bNPAU7A4o8NnFa9gtPtg+raViqGqTiLJkzzX9pdU+jdR7F1FzBgk11Wb0TEVNHJcNenbqqKxOOeujJ5rY6Ggku0sYlX+/Wws+iTrCj9MvmcxuupKh9fT6aobh+bFoXpwat6MwpyiKOTk5GQ0JwCfz5fx2LmSm5ub8apqYWHhRS9xEEIIIS4VsgI7hxQFlPAvJt5pW6BkvrI2diAVRXeBAfb4lrS2hWWkcPpLUTQH7rxanL5ikiO92ICv8moSw+2Y8dC0h3UGynHn1tC396dv3alqYNvYWCStEfb3/yeLc+6iKfQCaws+gqpqeB352LZFONnB8eFnJvV9LcveMCFEqopOtf96jg79dsZVWE1xsq7wI+inugZoqhMN59mdq7NQUFCA0+nM6AKt2tpaHI7p62kvlJKSEjo7Z+51C1BXVzcv8xNCCCEuBbICO5cUBxh9E+9LNoF7BRn/ruBaBqQoWP0/KLrqo+Svfi/eoqUoqo6ianjy69HcfgaPPsvg4cdJhrvBHluJy1/1blw5VVPMS8NbtIzcJW8DRcUZKEs/5MmtZWD0rSvfOyLbyXHXsDj37RjE2d//nzzV8nmeafszmkMvsjjnbu6q+Uf8zrFjONVs1hR8EJWJYUpTHNxa+WXy3PVTvk2n5uOmir8g21F0UT8OX7Jkyaxj8vPz8fv9F2E2E+m6zooVK/B6vTOOq6ysnJf5CSGEEJcKxZ6PYr+LLBwOEwgECIVCF/QHv2XFUZvvBr0I23c/ZG0CzQskUHq/CiNPz3oMu+iviQznMtKxGwBXsIKskpXo3jwAenf/iGDDLai6m8FDv2Fs3y/IrthIVtFybGwUINbfiGUk0JxZuPPqSEX7CLduA9smd+ldjLTvZLT3MLmr38320A/pHBmr0S32rmJz6Wc5PPgrVEVDQSWa6qVtZBuGNdb4v9p/PWsKPkhnZDflvg2oylg41xQn2mmlD7ZtY9pJwslOjg09wajRj666qfZdR1n2hrHnqBduxXUq4xsFHDlyZMo614KCAtavXz9vGxlYloVhGOzfv5/u7u4Jc3Q6ndTV1VFVVTXvu4UJIYQQc+1s8poE2DliWhZD8X7yrH6wc7H3vITd0QheP1QsRl25DqX9vWAOTX8Qzzqs4m/Qu/tnWEZ8wkPe4hX4KjbSt/enWKk4+aveTbTrTWJ9x9Jj/DVbcOfWMnzsGZz+EhTNgWUkiA82Tygt0NwB8le9m9HeRvT8Up7t/BIpa5SUGePeuu8ANsOJFvpiR7CxCLqqKPKupDX8Kvv6foRhx1mccw+LgnewrftbxI0hHKqHSv9m6gO3oigauuqaMP+UGUuXHeiqB3WGXq0XmmEYWJZFc3Mz/f39WJZFdnY2dXV1eL3eSyIcplIpbNumt7cX0zTxer3k5uYCZ3exlxBCCLFQSIA9w8UIsJaVwkoZqK0Hsd98ieT621FL6+iKDGDZNoX+fDTVxjP8rzD0vTOerYLvdqz8P2Xw6PMkQ1PXQPprrkPVXcQHm9Gz8nDnVNO/79G3juLwULj+d+jb9yhmbHjG+fqrN+MtXoaBQdKM4NC8YNsMJ9vZ0f0dIqmuCePdWpBV+feT467h+fYvYdop7q19+NROXW+1p9IUJ1tK/zeF3qXp2tZLlWmaWNZbtbxSUyqEEELMHwmwZ7goK7CmgdrfgXFsF/H1t/HzjkPs6GslZZnkuLwUun3U+PLYVFRDjsPCOfx9sEaw9RLw34cRH2W4afvYVrHT0Fx+Cta+j8RQO4qq4QyUkRhuZ6R9B6loPwA5i+/EGSilb+9/YiWjOLIL8RYuQXP5AJvU6CCjPYfAhoK17+PR47+DjcXq/PeT667nxY6/xmb6HZ7WFXyELEchr5z8W9YWfJikNcLBgf+eMEZB4/aqr5LjqknXt1qWiUkKXRkrGTCsBLrqmnHXrIstlUqhKAqpVApN01BVNf0lhBBCiAvrbPLa/H9WeplQjCRm+xGia2/hue4TLM8pYUtxHX6HhyzdSXt0CEWBLIebqGmg5DyITgJUB9igOsEVLMdMRLBSo1O+hpkIkxrpY7TnIInhdhTNibdwMbnL7iF04kXig80Yo4MomuPUBVsKqsPDaNcB4kOtgILTX0L+yneTjHRjY6OpLrBt6oK38kTL/54xvALs7fsP3lH3LXzOUiKpLoLOykljbEz29f2IzaV/iIKGqui0RbZxZOhXhBJtgEK+ZxFLcu6hOGsVuurCtm1Sp2pswUZTHDPWx85lSYJhGIyMjNDY2Dhhs4NAIEBdXR2FhYWXRFmBEEIIIcYsqJ/KBw4c4KMf/SjHjx/n4x//OH/7t397yTR0V2wLY/kWHLqTal8eewbaiZspgk4P1xTWUODJ5qcndnFoqIuNBVW8s3o1nuRhHL1/AVYczVGGL/hefGXvJdy2k2jXgSlfx0xGUfWxj+ZtM0m0az+JcBd5y+7BTEZBVTETIzj9JYSbX2O05+CE5yeGWoi0bcdfdQ2KXY5D8VDqW0t/7BgxY3DW92lhcGL4OeoDtzFq9GHaU2+e0D26n4Q5wv7+RynP3kiOuwrFBk11YVgx+mKH6YsdZkXee1maey+jxgDD8WbiZoihRCvZjgIagneiKlq6FGH8orDhRBuNw0+e2vTATZVvC+XZG4GzvyjMMAy6urrYt2/fpMdCoRC7d++mrKyMlStXSogVQgghLhEL5idyIpHgnnvu4Y477uCnP/0pv//7v8/3v/99PvrRj8731DBMAwuFPjPB/3vzWYaSE1dQX+k+Qa0vn08u3cLPm/eytaeJ/YOdfGH1beRk3Ywa+jEkhlF6/hL0h/GX/QugEO3aP+m1VM2JZU0MjUa0n0j7drLL1qG5feieHMJNrzDae3jqCdsm4ZbXUDSdtfkfRNc9dEX3TDk0y1FIQ/B2Cr0rcChuklaUoXgLAVcFfquU1shr05wVm0jyJAFnOS7Nh89RzE2Vf4mq6AwnWjk+/BRerYCG4B0YVpzu6N70LlxLc+7BwuCN7m9Tnr2B8uyr0t0Vnm//CoOJxgmv1DmyE6fm44ayzxN0VU+6gGwmsVhsyvA64fidnQSDQaqqqqScQAghhLgELJga2F/84hd87GMfo6OjA6/Xy759+3jwwQd59dVXZ33uha6BNc0Ug8k4X97zBAlz+t2RSrx+/mjVrTy05ykGElFKvH7+dM0dODt/D+K73hqoF2FV/he9e36OlRxJ363oLorWf5DeXT+a1KVAUR0UbfwQKDq2mcQy4tiWSWqkh2jXAYzRgUnzUTQnBRs+QNyKcGjwF5wIPZt+TEVnfdHvUp59Fc3hF2iLbCNljeLRgtQEbqTStwnLMnjl5N9h2ilGUt0kzPCE499W+RV01cOBgf+iM7IDCwMFldKsdSzOvZtcVy37B35G4/ATWPbE81bkXcnVxf+Lw4O/otC7DMs2cWsBfM4SBmLH8Tpyx1p8GX00hZ6nK7oPTXFwR9XXCLgqMvpzG29XlcnGAR6PhxtvvFE6AAghhBAXyGVZA7tv3z6uueaadJP3VatWcejQoSnHJhIJEolE+nY4HJ5y3FxJ2iY/a94zY3gF6BoN83pPM2+rWM6znUcYiEdpjgxSW/rPODo/DolT78foQYn8lqziJUTadqE6PKCoeAoWkRodxp1fTyrSnb5wC8Z27EpGetGcWYTbtmHGwyiqhitYSe6yt2NEBxk69jS2+dYuVLaZJDnYjie/Fr+zNH2/gsq1pZ9BReNXTZ/CtN86lxFO0hs7xJv9P+WWir/imuIHiZnD+JwldEf3cXTotwzEj6ErbgLOCh5v/kNi5lulCTYWndGddEZ3sjLvfdT4r+PE8DNYTDx3PaP7ebbtL7i18su82f9TVhe8n1+feJBK/ybWFX6E7d3fYdQYIOiqZGXe+1hf+DFeO/kNdvf+gC2lf4RD88z656aqKl1dXbOOg7GV2mg0KhsICCGEEJeABfN5aDgcpqamJn1bURQ0TWNoaHJf1YceeohAIJD+qqjIbEXuXFm2wv6B2VfxAF7uauSqwmoeWHYdX7vqPizbpjUaJV74fyaMU0KPklWylKINv0PBmvdRsOrd+CuvQtVduAIV5Cx5G/mr3oMrt/qteaRGGTm5l8RgC8boIKmRPkY6dtG7898x4sPkrbgXRZ3YKioZ6cYw4lT7r0dVxh6r9l+PRw/y2smvTwivp4sZgzzb9ufompuAs4yTI7uIpvrYUvpHNATvpMq/he7RNyeE1zPtH/hPRlK91AZunvLxUaOfA/2PUuG7hpFkN8VZq2gJv8yhgV9Q7b+ewfhxmkLP83TbF9jb9x/cWP6nJMwIxjRzPpNlWRPaaM0mky1ohRBCCHHhLZgAq+s6LtfE2ka3283o6OQr9r/whS8QCoXSX+3t7RdsXpZlcXI0hEVmlRh98RFM2+Jv9z7NX+16nMZQL2VZAQyt6NSWs6ek2lE0LwOHHqdnx7/Rs/MHdG//HqPdB3Bk5xEfbCLSvoNAzXVklawCQHNmgz1VILMJN7+KMTqIr3LjpEcVRWEw3sSi4B0ALMp5Gwf6/2vSquiZ4maIw4O/omNkB6FkO9WB6zk+/CxLcu9hdf4HODL0+Kzn48jQr2nIuWPax1sjr1HgXkQ01YfXkQ9A4/BTFHqX4tXz0uM6Rrazo+df2Fz6B4QSmf15q6p6VhcBSp9YIYQQ4tKwYEoIcnNzOXBg4pX5kUgEp3PyVecul2tS2L1QzHMoIR6PTOFUnMfbD7Crv43Prb4NI+cT6F2fOTXIBVZqQu3qWNeBNxnta6Rg1btw51Sj6m781ZvJLluLorsIZOfjLVpGtPsAieEOvAWL0NwBAIx4mKySlYTb3gBrrF2W7i/CVuDo0G+4uvhBNNWNU82me/TNjN5LU+h57qn5Z7Yf/zat4Ve5ueKvaA69RH3wVvpjR2d9fn/sKAoqfmc54WTHpMdNO8lA/DhuLYBtj805x11DzBjiloovYdgJRlP9NIVeoGNkB8vz3oVL82U0d8uyKC4uzqiMwO12k52dndFxhRBCCHFhLZgAu3HjRr773e+mb7e0tJBIJNLba84X07Yo9vhRFQUrgzBb6PGRtEzi5ludBLpjYf71yFY+uWTDW38g3mtJRSdvaqB7c8ld+naM2DDRrv0kw53YtoXDk4O3ZBWe3BpS0X6yy9YSrLuR+GALqZEebBuc2QUoqk7ukrsYOvokiqLhzq3i6PCTlPuu5vn2L3FLxV8xYvRAhivKCTOMYSdxatmMGgO80vm33FTxF4ym+inJWk1XdC8KKtmOInTVRcIcYdTon3CMuDGMU8ua9jUs2yTXXc2x4Se5rfKvcapZNA4/RW/sMLZtEnBV0BC8g3WFH6El/Ap1wVszmruu6zQ0NGQUYE8vXxFCCCHE/FowAfb6668nFArxwx/+kA996EN87Wtf49Zbb53/q8KVsbKA1bnl7BmY/aPrG0oaeK37xKR4eGi4i5gJHs9aiO3BzvkwVnzix9uaO0De8ncQbn6NWP/EVlKpaD+h488T9QTJXfYOIq2v48qpRNXd6XZco4DS8hqB6s3krbgPIxaiLfw6x4Z+y9uq/55i7yoS1gjYkO9Zgt9RAkAk1UNf7DDThVpV0dJdBELJdkKJNuLGMGXZV5HjqqE+eBuWbWJYMdx6kJgxROPwU7SEX8ayDVy6H4Xp/xwDrnIMK8H6wo/SOPw0R4Z+NeHxULKdtshWir2r2FTyGRQ788oYr9fL8uXLOXjw4LRjiouLqa6unv/vNSGEEEIACyjA6rrOI488wvvf/34+97nPYZomL7300nxPC4ei8WznEd5RtZLDw90TVlbPVOoNcHVhNV/d8+SUj7/a3czbfTegOhdjKYU4s30omgP71DEDNZsZ6dwzKbyezogNM3joN+SvfCc9u/6dgtXvpfiaT2Cl4sQHm4h2HWD4+PME6m7AndfAvta/A2xMO0Xj0FMoisryvHezseiTDMYbsW1YlHMXmurk2NATNA4/xelBNuiqxLASJK1o+r6W8Cs0BO8k21lIR2QHL3f+LcOJllOPKhR7V7Ik9x3UBm5iT+8PcWsBNpf+IVtP/hMOzYNL82FaSfpiR8hyFOLUfBhmnJbIy5PC6+m6R99ka9f/5bqy/z3tmDPpuk5FRQU+n4/GxkYGBt4q2cjOzqampoby8nIJr0IIIcQlZMEEWID77ruPxsZGdu7cyaZNmygoKJjvKWFhc3i4i1pfPn+48mYeOfwqA4nopHEN/gJ+d8lmfnp8J4OJqbeKjRhxDM81OByl9O/7JYHa63AFK4kPnEBz+XD4ihk6+vSsczJigyRCnXjy6xnp3I0rt5pwy1a8BYvJX/kuYv3HCDdvxZPfQJ3/ZrzOfNoir5HjrsbnLOGlzofojx2ZcMw89yLWFHyAQs9Stnb9U3ob1/rgHZwYfobTQ23cDJHlKGB37/dpCb98xuxsukffpHt0P+sKPsymkj/g2NCT9MUOc0P55xmKtxBOdqKrbtYXfhTTTmGYCRyal0MDj8363rtH9xFJdpHjzvwjf13XycvLIxgMYlkWyWQSTdNwOp0oiiKbFwghhBCXmAUVYAHKysooKyub72mkpSyTlTll/Kx5N7eXL+VP197B8XA/e/pP20q2qJZs3cWPjm9n/+DJaY+VrbvQHCWEWnZhJsKYiRFUx9g2qu78euL9x7GtmTsDjBvtPUx22VoGD/2GQO31DMV+S6TtDUY695C79C781dcS6zvG0vx7sFSbpuEX8Op5PN/+JUx7cruogfgxXuj4MteV/jFrCj7Inr4fUuBZRkX2VTzRMnHF06VlEzeGpwivp7PZ3fcDCrzLiKZ66R7dx56+f6c0ax3be74NgKY4qfJvYU3BB+mPHcOw4zMc7y1Hhn7NhsJPZNQLdpyiKOmtYqe6MFCI0yWTJjYwGIph25AbcKMoCi6nrNQLIcTFsOAC7KXGpWjcWr6EN/paeLrjMC+cPMZVBVVcW1RDscdP1Ejy8+a9HBzqwp7lwqgtRZWoipP4wFiJwFj5wFhg1RwejHjmGzKY8TCqw4NtGdi2haI6sK0Utplk8PDjFKx+39hWs6kkikOnNngTTzR/dsrwOs6yDV7v/mfuqfm/RJLdrMx/L6+d/EfiZmjCuFr/zTSHMynvsDk0+Bj1gdtoCj9Pc+hFVua9jyxHIdFUL6adpCn0PAOxRm6p/BK57noG48dnPWo4cZK3ej1ceSzLwrbts24TJmZnmhaxhMFLOzs43jqEaY39nVYVhdqKANdvqCDb40DXZdVeCCEuJAmw50nTNArdPu4oX8pTHYdJWSav9TSxrbeFhza+gxyXl9aRwVnD65JgEVmaSqzv6FjNq6LhClYQad8OgG0ZKFrmf1yKqmNbJigaiqJiW2/V5tpmimjXPty5tRixYUhoDOg9xMzJm0KcKWlG6Iq+yeKct/NSx1cZTDRNeDzbUUSep56XOh/KaJ6dkR1cW/z/0RB8G5qiE0q2Ue3bzMHBt8oFQsl29vX9mGW59/LqyX84/V1S7F1JWfYGHKqXlBVDUxyUZq9jJNVFV+hNbNsk372IPM8iFBQ0df56uRqGgaZpjO/ebFlWetX3fI1vyjA6OkpnZyemaeL1etObeEgP2/NnWhaRaJIf//Yw8YQ54THLtjneNkzryTDve9sScgNudE1CrBBCXCgSYOeArijcVbmCHFcWT7YfZDgZw7QtftW2n3sqV/Lgsuv5pwMvEJvmAq9Cj49PLtmEw04x3L4bAE9+PaloH+apVddkpAdfxQZG2ndmNCdXbg2pkV48+XUkQpP7q472HsVftYloz2Fsh0Z7fFvG77c98jqqok4Kry7Nz43lXySa6p9xJfd0FgambZDnriNhhkmYIyzNeyc+Zzn7+39K1OgDoDX8KqsL3o9HzyVmDFKWvZG1BR8iZY3SGn6FuBlhUfBObCxeaP8KoWTbhNfx6vmsLfgQJdlrcajujN/rXDAMg1gsxvHjx+nq6koH17KyMurr63E4HOcVZE3TJB6Ps2vXrknbJh85coTy8nKWL18uF6KdJ8uCx55rnBReT5cyLB57tpHffdfKizgzIYS48kiAnQO6poNpcE1hNZuLammO9NMTi+DSdJyqTqHHx5+tvZPfth9kR18rqVObCPgcLrYU13FH+TIcZoz+/b/ESsXQPTkEaq9jtPcoiu7CNhIkhtoI1F6PI7uA1EjfzBNSVLJKVhDtPkhWySoi7TsmDbHNJLZtEB9swlWyBMPKbPtVAMNOoCtvhUBNcVHtv46Vee8javTj0YPkuRsYiE/fLSE9VTRURWdHzyPp0OtQs1gUvINbq77Ci+1fIZRsx7Dj9I0eIc9dj0P1sjL/fbze9X9PtfeClXnvI2WN8lLHQ1PuIDZq9PNa19fZWPQAJa6NqDjRdZ14PE48Hsfv96Oq6pyvVBqGQXt7+6Q2XYZh0NraSnt7O+vXryc/P3/KgDm+sjr+mGmaaJo2oTQgkUjw6quvkkpN/gXJsiza2tpIJBKsW7dOQux56BscZSg8+9+TaCxFa1eY2vKAlHAIIcQFIgF2juiajs5YYKj3F9DgL8TCJppKsKO/lXpfIe+pWcv76tYTSsbQFBW/w41lxkl07GTo5D4URcVbtAxf5dVEOnbj8OZQtP6DpKKDYFsompOcRXfQt+9n2Ob0P0gDNddhjA7hLViEmYiQGGqbeqCikor24UpVk+3IvKNDlqOAXE89t1eOlQn4XeWkzCgWBnFjkIQZ4uriTwFwbPgJTgw/m+5acKZy31UMxI5NWLFNWVEODv6ckVQPN1X8BeFEB249iFPLwq0FyXYW8WzbF4mkxjYg0BQXDcHbebL187Nuf7ur91+5t/pqXn9tG263m6VLlxKLxXjjjTcoKipi2bJluN3uOQl6tm0TCo3VB9966604HA5M02RgYIDW1lb6+/uxLItdu3Zxww03kJWVNeG5pmnS2dlJU1MT0ehYZ4tgMEhdXR2FhYVomkYqlWL//v1ThtfT9fT0MDg4SH5+voSqc5BMmRw83j/7wFMOnRigvCgbl1P+iRVCiAtB/nWdY6e3XDJNg2zdxaaiOizbQlEUTMsiS3MykoxjqCm0ZBxndiHOxXfg8peSCJ9k8MhvSUXGduFSdBcOby6oOsG6G0hGushf9S7Cza+SGJ64cYLuycFXeRWaM4vh4y+Qv/Jd9B1+gqk2IHBkF2Gl4mOru31NNDTcxpGh32T0HusCt7Cz51+IpnppCL4NXXWxt+8/6IrunRBUCzxLWZV/P0XelWw9+Q1szvzoVWFx8C6ODj0+5eu0Rl6jyn8d0VQ/O3u/i6rorC/8GI3DT6bDK0CVbxN9sSOTdviaimUbnAg/R2XVco4eaaStrY1FixZx1113AWMro8lkEqfTed4h1jAMsrOzGRgYYOvWrcTjcXRdp7i4mBUrVmDbNm+88QbxeJzGxkZWrFiBruvYtk0ymWTr1q3p4DpueHiYXbt2kZeXx8aNGwEoLy+nqKiI3t5eent7p53PiRMnCAaDUg97DmzbJpmavnTgTIlTXQqEEEJcGBJgLyDHqYuuHCaYgKqoHBzuojsWxqFqrMgpwaG7CQQriXXvJ9T0ClZyZMIxbCNBMtJDTsOtpEb6GG58Dk9+A76qawjU3kAi3Am2jSO7AM2ZzWj3QYYan0V3+TFTsWlXarNKVhLtehOAZPgk2baTsuwNdI7MXGNb7F1FtqMIh+ql0reZoKuCZ9q+SMqa3Nu2L3aYF9q/zJayP2Jd4YfZ1fu9CY+vLfgQNjYdIxNLHFyan0rfJrIc+aTMUcqy17Gn7/soqARclbx28h8njM9x19I9un/GeZ+ue3Qva/Ou5YYbbmBkZIR9+/bR39+Pbdt4PB4qKiqoqak5rx6wpmkSjUZ54403JqyOJpNJ2traaGtrY/ny5dxwww3s37+fkydPsmrVKmBsFX/btm2TwuvpBgYGePPNN1m0aBE9PT24XC4aGhpYsWIFhw8fnnJ73MHBQSkhOEeqqpDlzby9WpbHgSor3UIIccFIgL0IHJqG49RWqctzSliZW8pQYpSYkURXNUwgq2QVmjOLkc69GKOndoNSVNx5tfgqNoKiMXRkbAevWH8jsf5GnIFysoqX4wpWEuncTbRzD9hjK6CunEpSIz1TzsedW4MrUEao+ZX0fVZ0iGtLfp+XOh5K15WeKc/dwDUln+bgwM8p8q6gLHs9T7Z8fsrwmj4uBq93/TP31P4/Dg78nKQ5wtLc+6jyb0ZVdHb2fDe9MuvS/Kwt+DBl2evoGNlBONlB0oyS7SziHbUP0xp6hYQZJm4OT3gNVdGx7Jk/Qp8wJzuF1+vl8IFjdHZ2TngsFotx7NgxWltbufbaa/F6vecUYg3DYNu2bRjG9CUNBw8exOVypVdjDcPA6XQyPDxMJBKZ9TVOnjzJkiVLGBkZ4eTJkzQ3NxMMBlm3bh0Oh4O2tomlI7Ztz0v5QCqVStftWtbY9+dCC9IOXWP14gJ2H5r679SZ1i4txOlYWO9xriVTZrrjhqIoV/z5EELMLQmwF5nz1Kqsz+Em6PSiKGDZY307nYFy8nOqsCwD20yhObOwzRSK7gZs8le9C2N0EDM5gqI6cPoKiQ800//mf2HEJrbAyipZydCx5ybcp2hOsoqXk1W6hsFDv8Y2xlZnVYcHd7CShBljS+kf0TN6kMbhJxiInwBscly1NARvpzRrLa93f5Ou6B6qfJvxOvKIpKbfmGFcyhqlc2QHd1V/HU11EEp00hs7hIKS3m3rxPBzLM69i46R7fyq6f8jddrWtIcGH8PnKGFj8QM4VS8K6oRShZgxhM9ZkvGfgc9ZSjyemBReT5dIJHj99de56aabzjrAGoZBU1PTjOF13PHjx8nJyWHlypWoqkoqlaKlpSXj1+ro6KC0tDTdfWB4eJht27axadMmhoaGMAyDoqKidKcD0zTnrHXXbAzDYGRkhBMnTtDb24tlWXi9XiorK6mqqkJV1QW1y1mWx0FFsY/27pl/uSjI9ZAXzHwTjctNMmUSTxrsOthDZ8/YJ0oFuR42rijG53XikCArhJgDEmDniVs/vQ5RwzJT2LaFbatozrGLecx4hGjPITz59STDXXgLF6M6vERP7scyYgyN9KRD6Ol8Vdei6m7ylt1NfKgFKxUbC6k5lcSH2ujf/3PM+NjFRarDQ97yd5Ac6UP35fKrpk9R5b+ODUWfwOcYC4UjqR5OhJ5ld+/3SVqnfiB5ltIafi3j99sW2UqBZxkvtX+VSHJi6C3yrmBj0ScZTQ2yt+8/pnx+JNXFix1f4ZaKL7E87z0cGHg0/Vhr+BVuqfw/vNn/Uyx79tC4OOdumg5Nc2HbaRKJsZBbUVFxVkFLVdVJq5/TCYfDJBIJUqkUubm52LZNLBbL+LVisRg5OTkT7hvvBbt+/XqcTifd3d3E43FUVcU0x1bFLnQdrGEYHD16lObm5gn3R6NRDh8+TFNTE5s2bcLj8SyYEOt0aLzjpjr+88mj9A9N/WcU9Ll4922L0LUrs3wgZZi8tqeTPYcn1mL3Do5y8PgAi6pyuGNLNQ5dQqwQ4vxIgL1EqJoDVXNgGclTZQAKqu7CV7EeBRXd7cc+9fFrdsU6Qk0vTwqvmstHdsUGPHl1hJu3kgh34slvILtsDWZylFDTq8T6G8G20JzZeIuX4S1ajhEPkRhswu3NJt+zmMbhJ2kcfnLG+WY5ChnIYFescYYVJ2YMTgqvAD2jB3iq9U+4peKvqA/ezvHhp6c8hmUbbOv6JrdVfZVDgz9Ph9VIqovhRCsNwds5OvTbGedR5F1Jll7AyZOZ1cy2trZSVlZ2ViHLtu1ZuwKcLhaLEQqFCAaDAGe1Qjq+qnq6YDBIeXk5x44do729Pf2xPYyVLRQVFbFmzRp0Xb8gJQWmadLW1jYpvJ4ukUiwdevWc1rhnk9Oh8b/vGsJB48PsPtQD8ORsb+Dviwna5cUsmpxAbp2Ze6AlkyZbN/fNSm8nu5Y6xCqqnDrtVVSUiCEOC8SYC8xqn76hSI2tmVi2SkURUNRHSiqhm3pBOtvwrZMkqFObNtEd+fg9Bdj2xaR9l2M9h4CwBgdxLZMRnuP4K+6hmDDzQBYRoJY3zEGDvyCYMMtGLFhrESMZbn3TeomMJmCz1mC15Gf8fvy6vkkzOk/ek1Zo2zr/n9cX/YnM7bdiqS6CCc7qfHfyInQs+n7d/b8C7dU/h8S5ggt4ZenfG6BZxlbSv43vd2DGc87Ho9nHEYMwyAajeLz+TI+PozVg0aj0fTWryUlJfT3Z9ayqbi4eELJgcPhYMOGDezdu5e+vqn7Bff09PDqq6+yZcuWC7YSe+LEiVnHJBIJOjo6qKysXDAhVlEUHLrGyoZ8ltfngz22x56qjn2PXMm7b9k27Do4e43wkeZBtqwrkwArhDgvV+6/tguAouqougvN4UHVnai6jqo70ZweVN2N5vLhzq3Fk78YZ7CMZLiHoaNPMdpzEFdOFXkr7sNfu4Vw6zbcOZUkR/roev07dG39Nj3bv0e4+VUUTUd1uEkMd6CoKqriYH3R76JM+62hsLHoE9i2SY3/BiCzcFcbuIm2yMwlB8OJVkZT/ZRmr5tx3MmRXazMfx9ZjsL0fSOpHp5v/xLL897NbZVfodp/HVmOQrx6PqVZ67ip7C/YUvzHdLb3ZFSbOk7TNAzDmPU5pmmyf/9+XnnlFSKRCPn5mYV7XdcJBoOMjIxg2zaqqlJeXp7RKqzP5yMrK4vu7u70fZWVlfT29k4bXsdFo1GOHj16VuciU0NDQyQSmW2M0draOmGFeKHQNBWHruJwaDgdGrqmXtHh1bRsDhzvx7Qyax6261APqbNoSyaEEGeSFdgFStVdWKaBorswY8OY0Th6Vg55y+4GwDJTmPERbDNFdulqot37Ge0+xISesIqKv3oz0a796Fl5ON05vNr5bVbkvZO7qr/OocFf0BbZimknURUHlb5rWRR8G5riIGWNrUxWZF9N+8jM29DmuGoJuCrpiGyf9X2djO4mz90wYzsvyzYZjrdyZ9XfMhBv5PjwsyTMME4tm6F4C6VZa1mScy/rC/Mx7QRJM0YipPP8yy+RnZ3Nhg0bMjrHMLbCmX5dy5pypXC83nP8orDW1laqq6szWkUtKyujv78fn8+XvmIbYP369Wzfvn3CfadzOBysXbuWxsbGCWOqqqrYtWtXRu+to6ODpUuXZjT2bMzU/utMo6OjC2b1VUzPNC36BqfvRnKm/qEYpmUjHYmFEOdKAuwCpp7qaKBm56OZBiin4qmtgG2je3MY6djJSMfkQKM6vAQbbsZKxYie3EdwyR20j+zk+rLPndoCVqU+eDsbix/AspJoqpPu6JscGPgZXdG9XFPyID5HKRuKPk7cDE3besvvLOf6sj9mV8+/zrpLFoBpJ3Ep/hnHBFxldI/uQ9c8JM0otYEbcaheUlaMrugetvd8G8OKsaHoEwwnWin0LMPvWIbP5yMUCpFIJCgqKqKnZ/aPO6uqqti5cyepVIqamhrKy8sntYCybXvCx/idnZ3U1dVRXl7OyZMnKSkpoby8HLfbjWVZhMNhWltbMU2TRYsWsX37dlavXp3+OF/TNHJycti8eTOHDh1icPCtkgdFUSgsLGTJkiX09PTQ2tqafmy808D47l+zGe8SEAgEMhqfqbOp4dU0bdqQLhYOBc6q7+14yYUQQpwrCbCXifEwO87GSbT3CNlla8kqXkG0az9GIoyiaLhzqnAFK4j2HCTS+gb+mi04A6WUKMV0jmxnJNmH15+Hqmj87NgH0FQXppWYUJe6rev/sbrgA9Q7b+OGsj+hM7qLY0NPnLqwy8bvLGdR8E6qA9dzfOiZWVdpx2U5CokZQ9M+7lC9lGdfjd9ZQTjZzhvdD0871rINHIqHsuwNxEdTXHXVVTidTmzbZvXq1bzyyiszXvG/bNkyYrEYQ0Nj89m/fz/d3d2sX78+HdIsy6KtrW1CCDNNk+3bt7Np0yaWL1/O0NAQra2tjI6O4vf7ycnJYcOGDaiqyuHDh/H7/Xi93gmvres6gUCAq666imQySSQSQVEUAoEAo6OjHD16dELpADChz2qmLsTH9wUFBSiKklEwLSoqkgB7GdA0laoyPwcy3G63osSHrsvKuxDi3EmAvUwpmo63YBGDh39LdsUGssvXYcTDmPEwiXAn4dZtuIIVFK77ACnN4PnOvyaUbMewxgLd4aFfcl/dIyzJvZvDg7+adHwbi719/05HZDs3ln+RYu9qSrPW4lC92NjEjSFOhJ5nf//PKPRk9jG1pjip8m3h6bYvTDtmRd57AEiaUbqi+1AVx7SbGASc5WTrxXR3dxONxKmsrGR0dJRkMonb7ea6667j4MGDdHV1TQhyPp+PhoYGvF4vb7zxxoRj9vX10djYSENDA7quY1kWIyMjZ740Hs9YH9Dt27cTj8epra1l1apVjIyMYBgGiUQCj8fD0qVLMU2TI0eOsGTJkgmrl4qioOs6uq6nA65lWTidzilXWVOpFLqu43A4Mu6CMD7PuaQoCkVFRZMC9lTq6+svWl9aceGoqkJdeRC3SyOemLm2VVUVVi8qvKJrhoUQ509+clzGVN1J7rK3Y8bDjHTsQc/Ow+Uvw51bjV21Cds2sBSblDnK0tx7OTTw3wwmmgDw6nkkzDC1gVvIchRxePCXRFNvtcfRFCdV/i2szn8/YBFKtuNUs3iq9fOAkt5dS1OcLM+9j6CriuFE6xSzfEt98HbCyc4Jr3O6ZbnvoiZwE8eHn8Oyk9QGbmJ94Uc5PvwsBwf+e0KJQrajmFx3HaZhYQVULGOA7du3T9jhqqKigqVLl6ZXSU3TxOPx4Ha7aWtrY9++fZNaVMFYjeuiRYsAptxuVlVV1q5dy86dO7Ftm82bN9Pe3s7LL79MPB5Pj/P5fNTV1VFQUEB3dzdVVVWzdjBQVRWPx8ONN97IwMAA7e3tJJNJnE4nlZWV2LZNRUUFTU1NMx4HIC8v74LsiOVwOFi1ahWhUGjGFe7Fixfjcrnm/PXF/LCxue3aan794swdKK5fX84V2GVMCDHHFPsK+PwuHA4TCAQIhUL4/TPXV16uLCPJ+AVciqqjqG8Fl6QZ5fjwc9QGrse0k0RTA7g0H5ri5KnWz7Ms7z5q/DcSSrQTNfrRVRdFnuVEU334nKWMGgN49VwSZoTXu/+Z/tjRCa9dkX0Naws/zIsdf0042THl/Kp8m7mq+H9h2SaD8eMcH36GULIDVdEp8q5gcc5dxI0Qr3T+LTHzrRKDbEcxaws/jKbovNz5N+nesJtL/4gS72oU28GhQ4em3FhAURRuvfVWjhw5wooVKzh8+DCRSITBwcFZP9a+6qqrKCwsxLZt+vv7J6zUlpeXU1JSwoEDB9i8eTP79u2bsStAfX09paWlNDU1sWLFioxXJMe3nx03XkObSCR48cUXZ1yFVRSFTZs2EQwGz6lnqWVZE7aFPfMYlmVhGAYHDhygq6trwvn0er0sWrSI4uJiWX29zCRTJu3dEZ59vZVobOL3n9ulsWVdOUtrcmU3LiHElM4mr0mAFdi2hWEnODL4awZjTRR5l+NzllKYtYyfH/8Ylm2gKU6KvCtwaX5MO0l/7BgAt1c9RMocRVMd7O79AesKP8xz7X81aRW1yreZ9UUfpyOyneOhZwgnOlAUjULvMpbm3EO2s5iEMUJ/7Ai9sYPUBG7Eq+fhULNIWaNs7/42/fGjU00fBZVNpX9A3Aixu/ff2FD0cSp81zIcb8cMBdi9e/eUz3M6ndx4443pnameeOKJjOsxV69eTUVFBTBW8/rCCy+kV1evueYampubKSgoIJlMcuzYsVmPd80119DX10d9ff1592Y1TZNoNMq2bdtIJpOTHh9fIS4oKDjrAGkYBoqi0NnZyfDwMAD5+fnpWtYzj2cYBrZtMzAwgGmaZGVl4fP5ply5FpcHwxj7xaa9O0Jr19gWxyX5WdRVBLGxZRcuIcS0ziavyfKHQFFUHIqHpbn3oqAQN0NYtoltW1RkX01r5DVMO8nJ6MQgqKBi2xZ7en/IxuJP0DHyBi4tm9sqv8LRocdpCr1Awhz7AdY5sguPnsfS3HspyVqDS/Nh2SmGEq00Dj/NSKqXm8q/iK6uYkfvI7RGXsOj5/K26r/n8eYvYNjxqaYOjNXj7uh+hHfUfYsq32YAjg8/S3X2Dew+cWCG9z0Wovbs2cPq1atxOp0Z9y91Op0Tbq9bt45t27ZhWRYej4dYLEZpaSkvvzz1pgpnampqYvHixRmNnY2maWRnZ3PzzTfT2dlJW1sbiUQCXdcpLS2luroaVVXPKby2tbVx9OjRCaUVbW1tOBwOVqxYQVFR0YTjjv//6e3IxOVt/OKsmvIAlSU+bEBTlStydzIhxIUjAVak6epYPWKWWpC+b2X+++gY2YFpT17Js7E4EXqW0ux1aKoTj57LidBzDMZP0BB8G3fX/BOjxiC2bZHlyKd7dD+vnvwHQok27qn9Z37V9CApa5QcVy03lX+R7tF9+J3lFHiW0Bc7Ql3gFlrCr8wYXsclrRFOjuyhyLuC7ug+avzXo1qeKS92CgaD+P1+gsEg8Xic3t5eent7KS0tnXH70/R50vUJGxVomobf7+faa6/l6NGj2LaN3+8nEolMqHmdSW9vL+vXr5+zK/JVVUVVVSoqKigrK0t3KFBV9ZzqXg3DoKWlhSNHjkz5eCqVYs+ePaxZs0ZKAy6iRNJM15PagOsS+2hekwu1hBAXiPyUETPy6LncUP4FXur4GqY9eXWyOfQSb6v+O7pH36QucDMHBv6LoUQL23seZnff98nS81EUhdHUIElr7Gr9JTl30zN6iCLvCmoDt5DvXkzSCrO1659Ymfc+ri7+FE+2fp4cVw1N4ecznuvJ6C4Kvcsw7SR7+n7Act8HJzxeVlZGXV0dqqoyMDAAQDKZ5JZbbqG3t5fq6mq8Xi85OTmoqkoymeTkyZN0dnZOWHGsqKiYFDRVVcXn87Fu3Tps2yYYDJ71LleWZc3Z1q6WZWHbNiMjI4yOjqJpGrm5uecckC3L4ujRqUs4Tnfw4EFKSkrO6TUuBNM007W6410dLgcpw6R/KMbOgz30DkRBUSjJz2LjimKCfpd8TC+EuOxdHv+aiwtGV13kuRdxX913aBx+iubwSyTMCC7VR03gehqCd6CgUZK1lkLvctoi29IXahlWjFCyfcLxshyFLM97DwkzzIbCj2PaSULJNtoiW1HQqA3cSM/oIW6t+BIpazR9UVYmLNtgJNnN9p5vk+0owpnrTj+2dOlSCgoKOHjwYDq8pueUlcXSpUtxOp2YpsmBAwfSHQkqKipYsmQJBw4c4OTJk+Tl5bF48eIJQcg0TTo7O2lubiYSiZCXl8eqVasybmUFnPPK6FQMw6C7u5tjx44xOvrW7kiqqlJWVsayZcvQdT3jj3RN06S1tTWj8JtKpeju7qakpGRea1wNw8CyLFpaWhgYGMCyrHTXB5fLtaCDbMow+c2LTTR3Tvx0IRRJcKR5kMXVOdy+uQaH9FkVQlzGFu6/4uKi0VUn4GRJzt0synkbKhoWJioamnpaLahpc1vll3nt5D/SPfrmpOMUeJZybcnvs7fv3zkReg4VB++s/xe8jjxe7vwbqvyb6Y8dY2vXP1IfvJ2Vee/F7yyjK7o3o3n6naWnNlKAkVQPKXuE3NxcfD4f+fn5bN26dcpV0Wg0ys6dO1m5ciXZ2dnpi5MikQi9vb34fD6uuuoqysrKyM3NnRD8DMNg165dE7oMDAwMkEql8Hq9ZGdnT9kn9kylpaXTblV7JtM00xsFjJcGjHcCMAyDpqamKS8csyyL9vZ2BgcH2bJlS8arvZZlTQr9M+nv76eoqGjeAux4ucN4Oce4oaEh2traKC4uZu3atRekhdiFlkyZPPVay6TwerqjLUO4nBrXb6jAeYmVFAghxFyRACsypqlOxn8cTvVjUdfGVjw3lf4hCTNMc+hFEmYYp5ZNlW8zmupid++/0TGyHYDirJUoKIBCyopSF7iFA/0/A+D48NOEE51sLPoER4cen3VuCiq1gVt4ufNv0vc1hh6npu5Ogv48duzYMetH+gcOHOCWW26ZFDojkQjbtm3juuuu4/jx49TU1KBpGoZhcPTo0SlbZO3cuZPrrruOuro69u3bN/PcFSWjhv6WZWGaJs3NzbS2tqYvOMvJyUn3k00kErN2PYhGoxw4cIAVK1ZkHGLPpvRgPhubGIZBR0fHtLW6AN3d3ezevZu1a9cuuJXYWMKgsXX6nerG7W/sZ/O68oswIyGEmB/yGZOYc5rioD38Ok4tm1x3PW4twJ6+f+fx5j9Ih1en5mNj0SdxaF5AQVOcZDuKGEq8dRFVb+wgFiYVvmtnfc2awI2MGgMMJ1rS9zWFX8QXdJFIJAiHw7Mew7Zt2traqK6unvRYNBqlq6trQvsnRVGm7C8LEI/Hee211ygsLKS2tnba11QUhbVr1+J2u6cdA2PhNRaL8cILL3Ds2LEJ3RKGhobYuXMnu3btwuFwkJ2dPet7HX8vmRjfwjZTgUBg3lZfFUXJqFa3p6dnxk0WLkWplMmugz0ZjbVt2HekF9Oc+62ChRDiUiABVsw5XXWxOPduEkaYPb0/YE/fD+kZ3c/4Rgp57kXcWfU3uPSxHm+mlaDSdy02NjAxVG3r+iYbCn+X0qz1075ehe9a1hV8hO3dD0+437QTdEZ3ZLSl6bienh6CweCUjzU3N1NRUZEOfr29vVPu1DVudHSUV155hYqKCq6++moKCsa6OwQCAVavXs3tt9/O2972NgoLC2cNk7Zts3Xr1in7uo7r7e3l2LFjLFmyZJZ3ORaI+/sz27de13VqamoyGquqKuXl5RiGke4Be7HYtk13d3fGtccnTpw46wvt5pNp2QyFM+tqATAYimOal32bbyHEFWphfX4mFgxddbE8/z2syH8P7ZE3CCc70RUXlf5NuPUgmuJCVcZ+f3JoHpbmvpNwspM8TwOdIzvSxxlKNPNS50NsKf3fjCTvpnH4qVOrtAp57nqW5L4Dt+YnZgwSTnZOmodtM2PIPNNMdaiRSGTCSmkmPWPj8Xg6xK5btw4gXQZw9OhRkskkLpeL8vJyqqur0XV9Um2mZVmcPHkyo9dra2tj0aJF6V60Mzmb8OZwOKioqKC9vX3GcXV1dQwODrJjxw5ycnJoaGggNzf3onxUb1lWun45E+FweF7LHc6WooCqZt5LVVWVM38fFEKIy4YEWHHBONSxsFcTuAHLNlFQp11pzHLkMZrqpyF4+4QACzAYP8Gvm/4/yrM3Uhe8lWxHEYqi4lSz0BQnNja66iHgrJjU9SBuDhLIynyDAI/HM+0q53iwHf/vmZsZTMeyLHp7e1myZAkDAwPs3r073doJIBaL0djYyIkTJ9iwYcOkwGea5rSlClO9VldXF8XFxbP2tPV6vRkdE8ZWYVesWIFt23R0TL0dcF1dHRUVFbz22mvAWGnD9u3baWhooK6u7oKHWEU5u2b5C62xvq6r1JQFaO6Y/gKu09VVBNClD6sQ4jIl/7qJi0JVtBkDg666KfAuJs+9iLLsDZMetzFpH9nGix1f4bfNf0jCCKGrbjTVga460RQnS3PvnfS8tpHXKS3LvKVTZWUlnZ2TV3IB8vLyJqzmFhYWZnzcJUuWEI1G2bVr14TwejrLsti5c+ekzQ8URcl4QwQYW/Wd7eIst9t9VnWtMLZhw4oVK7jllluorq4mNzeX3Nxc6urquPnmmyksLGTr1q2TVoobGxsZGhq64KudqqqmyzQycWZHiUudpqosq8tL73Q1E49Lp6YseFYrtkIIsZBIgBWXDF1141DdbCr5DDX+G1Gm+PZ0a0FuqvgLAq5KVOWtj9o1VafCdw31gdsnjI+mehmINVJVVTnr6/t8PnJzc6cNsLW1tZOCYVlZ2azHVRSF/Px8GhsbZw1xlmVx7NixCXWctm2f1eqlruuzlk3U19dnfLwzj+3xeFiyZAkbNmxg6dKleL1edu3axeuvv048HqegoICamhpqa2spLi5GVVWOHz9+UepNc3NzZ70gblxtbe2C60IAcNu1VTM+rihw55YaxmvOhRDicrTw/vUWlzVFUdEVN+uLPsaagt8Za6eV7ERTnZRnX02xdwXAxP6zp+iqi7WFv0NJ1moODf6SgfhYO6ld/d/l9kVfI5EY21lrKtnZ2Vx11VXs379/yhXS4uJicnJyJr6errN8+XIikci0tZeKorB+/XoURaG3tzejc9DV1cWqVavSt1VVpbi4mEgkktHzS0tLZ2zdVVNTQ3l5+Xn1QdV1Pd1p4fTjVldXk0qlGBwcxLZtCgoKWLlyZcYlEHNh1apVbN++fcYxU/0yshA4HRp1FUHecVMdL2xvJxKdWO4S9Lm49doqSgqy0GU3LiHEZUyxF9JVDOcoHA4TCAQIhUL4/f75no44C6adwrIMUEBXXCjK7B8aWLaFaSexbAPDiuFUswAVxdYJh8OcOHGCgYEBbNsmOzub2tpaioqK0nWop1NVlaqqKpYsWTJt4DMMg+bmZlpaWiZ8fF5YWEhdXR0w9vH7q6++mvH7vuWWW/B4POnbyWSSZ555ZtYV3IKCgvTFYs3NzbS1tRGPx1FVlcLCQurr68nOzp6TlcdIJMJLL70EwJo1a/B6vRw8eJBQaGKNZlZWFkuWLCEvLy/juuHzYRgGAwMDvPnmm5PKGVRVpb6+fsGuvo4zTAts6OobobN3rGdxZYmfglwvqgKa1L4KIRags8lrEmDFFSeVSqGqanoXq/E61vFeq52dnZimSVZWFuXlY83gZws74x/Zx+NxTNPE4XAQj8dpbW0lHA6zZs0aXn755YzneNttt+FyudK3DcOgt7eX3bt3T/scj8fDddddlw6J43Maf3+GYczpqmM8HufZZ5+loaGBvLw8tm/fPm19L8DatWspKiq6KMFx/L339/fT29uLbdv4/f6M/zwXkvF/whdSPa8QQkzlbPLa5fOvuBAZOj3EnX4Rlqqq+Hw+Fi9enA62mYaC8dXZrKwsUqkUr7zyCqOjo+njulyujFpbjR/jzICl6zqFhYVce+21HDlyhKGht3ZjUlWVsrIyli5diq7r6R27zgyrc/2Rua7r5OTkUFNTwyuvvDJjeAXYt28ft99++4xj5sr4n0dhYSH5+fnYto2qqvO2wcKFJMFVCHElkgArxBkURTmv+lBVVdPhFcZWdjs6Oqiqqppxi9NxtbW1U4YSXdfJzc3l6quvJplMEo1GUVWVQCBALBbj4MGDnDx5Mn01/lyWC0xFVVWWLVvG4OBgRsHcsiza29upqqq6aEHyfP8shRBCXJokwAoxx2zbJj8/f8JOVy0tLWzZsoXe3l4GBwenfW5+fj7l5eXTBjxFUdB1HV3XcbvdJJNJXn/99Ql1p6Zp0t3dTXd3N1VVVemV2bmmqirZ2dnT9oWdSnd394zvT8w907KwLMAe2+tOVRWwyagdlxBCXKokwAoxxzRNo66ubkKAjcVi7N69m/Xr13Ps2DE6OjomtLrSdZ2KigoWL16c8Yqhbdu8+uqrM/aIbW1txePxpHf5mmvjdcSZsiwL27ZJpVILsgvAQpMyTA6dGGD3oR6GwmMXtGV7HaxeXMjapYXomiK/TAghFqQFE2A//elP881vfjN9u66ujuPHj8/jjISYmqIo6drQ03fDGhgYYNu2bSxatIjFixfT19eHYRi4XC4KCgrOqt+rZVl0dnZmtMHBiRMnqKmpOef3MxNVVcnKysp4vNfrZWBggM7OTurq6vD5fJfVBVWXkkTS5GdPHaV3cHTC/SOjKV7b08n+xj7+511L8bgU2fBACLHgLJhfvXft2sXjjz/O0NAQQ0ND7NmzZ76nJMS0dF1n8eLFrFmzhuzs7PT9kUiEgwcP0tnZSUlJCVVVVRQXF6Np2lkFOdM0aW1tzWhsKpWasBo8l1RVpbKyMuMLicZ3Ouvu7ua1117j0KFDF2WDgytNMmXy25ebJoXX04VHkvzX00exrMu+EY0Q4jK0IJY+DMPgwIEDXH/99RPCgBCXMl3XKS0tpbi4mHg8TiKRwOFwpFcsz+ej2zMvFJvNyMgIRUVF5/x6M1EUhYqKilk3K8jLy8Pr9dLT05O+r62tDa/Xe8FKHK5U8aRBc2do1nEDw3G6+6OUF/suwqyEEGLuLIgV2DfffBPbtlmzZg0ej4c777zzou7sI8S5UlUVXdfJzs4mLy8Pv9+PpmnnfWW8bdtndYwLWW/qcDhYvnw5JSUl047Jyclh3bp17N27d9JmDCdOnJBWUHPIMC3ePNqX8fi9R3pJJGUVXAixsFxSAfa+++4jGAxO+vr1r3/N8uXL+clPfsKhQ4dwOBw88MAD0x4nkUgQDocnfAlxuTmbFdULtfo6TtM01qxZw+bNmykuLsblcuF0OsnLy2P9+vVs2LCBvXv3MjAwMOm5F7LE4UpkWTYjo6mMx0djKS7/7WyEEJebS+ozu+985ztT9pPMzc3lL//yL9O3v/nNb1JbW0s4HJ5yp4aHHnqIL33pSxd0rkLMJ13Xqaury6gOtrCw8KJcaa5pGjk5OaxevRpFUTBNk1gsRltbG3v37p3QdeFM0Wj0gs/vSqEq4HJkvjrvdGogC+BCiAXmkgqwma4SBYNBLMuiq6trygD7hS98gc9+9rPp2+FwmIqKijmbpxCXAqfTydKlSzl8+PC0Y9xuN6tXr76oLascDgeGYfDKK69k1CUBLq+tXeebrmssq89jz5HejMYvq83DqctmD0KIheWSKiGYzmc/+1keffTR9O0dO3agquq0odTlcuH3+yd8CXG50XWdqqoq1q5di9frnfCYoigUFxdz3XXXzVu/1fLy8ozHXugShytNbsBNfo5n1nEel05dRVDaaAkhFpwFseyxZs0a/uzP/ozi4mIMw+DTn/40H/nIRyb90BbiSqPrOiUlJRQXFxOJRBgZGUHTNPLy8lAU5aKG1/ESgUQigWVZ1NXVUVBQQGtrKydPnpz2eRerxOFKoqkK991cz48eP0wsPvUFWrqu8s5bG6R8QAixIC2IAPuhD32Iw4cPc++99+Lz+XjnO9/JV7/61fmelhCXhPHwN37R43wwDIPm5mZaW1vTZQOKolBUVERdXR0lJSXs2bNn0q5d81HicCVQVZUsj4MPvWM5L+9s51jLEOapfq+KArXlQa5bX44/y4muXR6/PKQMEwWF4Ugc07TxZ7tQVQWXU8ojhLgcKfaZPW0uQ+FwmEAgQCgUknICIeaYYRi8/vrrhEJT9x1VFIX169eTSqXYt29f+r7i4mJWrlyJruuyAnsBJVNjK+ODwzEsG3IC7rELvZwLYv1iVpZlkzIsXt93koPH+0kkx96vokB1aYAt68oI+l04pM5XiEve2eQ1CbBCiHM2vslIR0fHjONUVeXWW29laGgI27bJy8sDLmx/WrEwGIaFaVljJS+6etY9geNJg588foSh8NQXDKqqwr0311NelC0hVohL3NnktcvjV3AhxLywbXvG+tZxlmXR0tJCXV3deW/iIGY2viZxqW8OkUyZJJMme470EhpJ4NBVFlfnUl7kQ1FAy6C0IZE0eerVlmnDK4yt0P76heM88N7Vczl9IcQ8kwArxBXAtm1M00RRFCzLQtM0LMs67/ZVPT09k+pap9PZ2Ultbe15vZ6YmmlZWJbNYChOc0cI07IpyvNSXRrABhz6pVWikUqZPPVaC42tQxPuP3h8gGyvg3fcVE9e0D3riqlpWjR1DM/6eoZps+9YH+uWFl02Nb9CXOkkwApxmTMMg6GhIY4fP57eCUtVVUpLS2loaMDtdp/zqmgikch4bCqVuuRXBReilGEyGIrz5KvNDAxPXIn0uHU2ryllaW0ejrPY3OBCShkWv3j+OO3dkSkfHxlN8eiTR/ifb19KftAz4/fMkZbBjHcRO9I0yOpFhUgVgRCXB/lVVIjLmGEYHDlyhDfeeGPCNq6WZdHR0cFLL71Ef3//jLtkzcTtdmc81ul0cgWU3F9UpmkxMBznp789Mim8AsTiBs9ua2PPkd70xVzzybZt2rvC04bXcYZp8/wbbaSM6Vf3bduetkXYVBJJE/n9SYjLhwRYIS5TlmXR2dlJS0vLjGN27dp1ViuppysqKsq4g0B5ebmswM4xy4YnXmlKt8iazqu7O2cMgxdLMmWx82BPRmM7e0aIJaYPqIqi4MtyZvzaWR5Hxqu1QohLnwRYIS5Ttm1z4sSJWcdZlsXx48cxjMxXs06XyY5bmqZRVVUlF3DNsf6hUYbCmf3ysftQDyljfldhHbpKd/9IxuO7+6IzPr64Ohctw13EVjbkX3K1wEKIcyd/m4W4TEWjUUZHRzMa29nZeU69WHVdZ9myZeTm5k47RlVVrrrqKun1Oscsy+Z423DG41tPhrFmWam9GM5mFXS2khNFgaV1ebMex+PSWVKbK1vmCnEZkZ8oQlymxnfEyoRpmudcn6rrOldddRUrVqwgKysrfb+qqpSXl3PjjTcSDAZl9XWO2baNdRZ/ZmPhdX4DnGFaFOZlvgV4UX7WjI87HRo3baygvMg37RiXU+M9dyxClfIVIS4r0oVAiMvU2WwSoCjKea2Q6rpOZWUlFRUVGIaBZVnpi7bOt1WXmJqmqRTkeDIenxNwA/O7AqtrKuuXFfGbl5pmHVucn0WWZ/bvYYdD4523NnC0eZBdh7rTF7O5HBrL6/O4amUJTqeWUV9ZIcTCIT9ZhLhM+f1+nE4nyWRy1rHFxcWYpnleYXM8AMtK68XTUJXD82+0kUzNfoHW+uVF8759rKoq1JYHKc7Port/+vpWVVG46aqKjHu2OnSVpXW5LK7JwbLAsm0cuopl2TgvkfZhQoi5Jb+SCnGZsm2bysrKjMbW19fLSukCZNuwcUXJrONKC7MpzMn8o/sLSddV3n3bIiqKp/7Y3+UcW1HNz/GcVc2qpqo4dA2XU8Pj0tE1VcKrEJcx+YklxGVK13Xq6+sZGhqa0AP2TIsXL55QuyoWDqdDY92yQuIJg12Hpm5PVVqQzbtubUC/hK7Adzk17rulnvBIkt2HewiPJNE1lUXVOTRU5gA2uuw4IISYgWJfAZ3Fw+EwgUCAUCiE3++f7+kIcVEZhkFzczMtLS0T+r0GAgEaGhrIz8+X1dcFLpkyicUNdhzopr07gmXb5AXcbFheTHF+1iUVXs+UTJlYto3CWCmAdKsQ4sp1NnlNAqwQV4Dxnbai0SiGYeB2u3E6naiqBIbLSTJlYttj7aVs2573mlchhDgbZ5PX5F83Ia4A4xdWyS9wlzep+RRCXClk6UUIIYQQQiwoEmCFEEIIIcSCIgFWCCGEEEIsKBJghRBCCCHEgiIBVgghhBBCLCgSYIUQQgghxIIiAVYIIYQQQiwoEmCFEEIIIcSCIgFWCCGEEEIsKBJghRBCCCHEgiIBVgghhBBCLCgSYIUQQgghxIIiAVYIIYQQQiwoEmCFEEIIIcSCIgFWCCGEEEIsKBJghRBCCCHEgiIBVgghhBBCLCgSYIUQQgghxIIiAVYIIYQQQiwoEmCFEEIIIcSCIgFWCCGEEEIsKPp8T0AIIYQQUzMtG9O0UBRw6Np8T0eIS4YEWCGEEOISk0yZmJbNm0f7GI7Ecegqi6pzKcrLQlVA0+QDVHFlkwArhBBCXEJShskL29s4dGIA237r/r1H+gj4XNxzYx25fje6LiFWXLnku18IIcQVJZkysSyb1Kn/JpPmfE8pLWVY/OalJg4enxhex4UiCX762yOERhLYUw0Q4gohK7BCCCGuCCnDJBoz2P5mF0eaBzFO1ZbWlge5amUx+UEPDkfmdaa2bZNMWThOrYQapoWuqaiqcs5z7OobobkjNOMYw7R4dlsr77ylAedZzFeIy8kltwI7MDBATU0NLS0tE+4/cOAAGzduJCcnh8997nPym6cQQoiMpVImrSfDfP8XBzhwvB/DtACwbTjRPsxPfnuEHQe7SaYyW401DIum9hC/eL6Rf/7Rbv75R7v572eOcaJ9mJRhndMcE0mDnQe7Mxrb2TNCPGGc0+sIcTm4pAJsf38/d99996TwmkgkuOeee1i/fj07d+7k0KFDfP/735+XOQohhFh4RmIpfvNSE5Y1/eLHtn1dtHSGsKyZA2gyZfLfzx7jly8cp7NnZKxTgGXT1Rfl1y+e4L+ePppxED6drqmc7B3JeHxnT+ZjhbjcXFIB9v777+f++++fdP8TTzxBKBTi61//OnV1dXz1q1/lX//1X6c9TiKRIBwOT/gSQghxZUqmTN54s2vG8DrujTe7MM3pxyVTJr99pWnG8DgeZJMpE9M8u9XYDKZ42lj5JFJcuS6pAPvII4/wmc98ZtL9+/bt45prrsHr9QKwatUqDh06NO1xHnroIQKBQPqroqLigs1ZCCHEpU1TFY61DGY0tm8oxmh8+o/m4wmDpvaZa1QBWk+GGQrHGY0bGYdY07QpyPFkNBagKM+b8VghLjcXPcDed999BIPBSV/f/OY3qa2tnfI54XCYmpqa9G1FUdA0jaGhoSnHf+ELXyAUCqW/2tvbL8h7EUIIcekzTAtjhlXVM0VjqSnvTxkme470ZnycA439dPREMDNcVtV1lfXLijIam5/jIeBzZTwXIS43F70LwXe+8x1isdik+3Nzc6d9jq7ruFwT/6K63W5GR0fJycmZNN7lck0aL4QQ4sp0tk3/nY6px1uWTTiSyPg44ZEkNeUa+4/1sXpJIfos81BVhdryIIW5XnoHR6cdpyhw48aK8+p2IMRCd9EDbFFRZr9dni43N5cDBw5MuC8SieB0OudqWkIIIS5TlmVTWeKnrWv26yGyvQ6CPvfUDyrKWW0eoOsqhmGx72gfqxYXZPyc/3HHIh579jgn+ybX2eq6ytu21FCSn4WmXlJVgEJcVAuiD+zGjRv57ne/m77d0tJCIpGYcdVWCCGEAHDoKletLM4owK5eXADTLGw6T23nergps3ra2vIAXX1RQiOJWVdfT+dy6rz79kUMhmLsPtRDaCSJrqnUVwZZVpeHAmfVr1aIy9GC+PXt+uuvJxQK8cMf/hCAr33ta9x6661omvwFFkIIMTNFUSjJz2LdLPWllSV+1i0rmjZsKopCVYmfLI9j1td0u3TqKoIcPNGPrqln1V0AxkJ3UV4WN19dxX231HP3DbWsXJSP06FJeBWCBbICq+s6jzzyCO9///v53Oc+h2mavPTSS/M9LSGEEAuEw6GxeU0pBTketu/vYij8Vi1rlsfB6sUFrF9ehEOfJRwq8I6b6nj0yaPTXpylqgpv21LDgeP9xBMmDVU5GIaF5jz74Ok6h+cIcSVQ7AW0pVVnZyc7d+5k06ZNFBRkVk8EY10MAoEAoVAIv99/AWcohBDiUmZaFpYFkWiC6GgKp1MjL+DBxp49vJ6SMkyGwgmef6Nt0sYDJQVZXLeunNF4isdfbsK24f1vX0pxftaFeDtCXFbOJq8tqAB7riTACiGEmEuWZZMyLCLRBN39o4BNYV4WDl1lz+Fe9p5qt7V2aSFb1pbJx/5CZOBs8tqCKCEQQgghLiWqquDQFXxZTjp6IvQOxjh0YpCOnggAXrfOxpUlrFqUn/HKrhAicxJghRBCiHOgqioup8rS2nwW19i0dIZYXJNDjt9NaUH2WZUlCCHOjgRYIYQQ4jyMX2i1tDZvnmcixJVjQbTREkIIIYQQYpyswAohhBDivCVTJpqqjP33VC9dXVNly1txQUiAFUIIIcQ5Sxkmo3GDbftOcrR5CMO0ACjOz2L98iJqy4M4zmILXiEyIQFWCCGEEOckZVh09UX5xXONGObErpzd/VEef6mJhqoc7txSLRe0iTklvxIJIYQQ4pykDJNfPHd8Ung9XWPrEDsP9pAyrIs4M3G5kwArhBBCiLOWMix2H+pJlwzMZO/hXqQSVswlCbBCCCGEOGsKcOjEQEZjYwmDk30jsw8UIkMSYIUQQghx1jRNIRpLZTx+ZDTzsULMRgKsEEIIIc6aadm4nJlfC+5xyUVcYu5IgBVCCCHEWbMsm0XVORmNdegq5cW+CzwjcSWRACuEEEKIs+Z0aGxcUYySwdVZy+rysKdvVCDEWZMAK4QQQohz4nXp3HZt1YxjSgqyuH59OU6HlBCIuSMbGQghhBDinDgcGouqcwn63Gzd20lHz1udBrI8DlYtKmDDiiLZxEDMOQmwQgghhDhnTodGWVE2993SQMqwGBlNomsqgWwXYKNLeBUXgARYIYQQQpwXRVFwOjScDo0sj2O+pyOuAFIDK4QQQgghFhQJsEIIIYQQYkGRACuEEEIIIRYUCbBCCCGEEGJBkQArhBBCCCEWFAmwQgghhBBiQZEAK4QQQgghFhQJsEIIIYQQYkGRACuEEEIIIRYUCbBCCCGEEGJBkQArhBBCCCEWFAmwQgghhBBiQZEAK4QQQgghFhQJsEIIIYQQYkGRACuEEEIIIRYUCbBCCCGEEGJBkQArhBBCCCEWFH2+JyCEEEIIIS4tiaSBrqmgKJimha4pqOqls+4pAVYIIYQQQgCQMiy6+kbYebCbzp4RbNsmP8fLumVF1FcE0TQFRVHme5oSYIUQQgghBKQMk9++3MyJ9uEJ93f3R/nty03k53h47x2LcbvmPz5eOmvBQgghhBBiXiRTJi9sb58UXk/XPxTjv585RsqwLt7EpnHJBdiBgQFqampoaWmZcP+nP/1pFEVJf9XX18/PBIUQQgghLjOWZXPo+MCs43oGRukbHL0IM5rZ/K8Bn6a/v5977rlnUngF2LVrF48//jibNm0CQNO0izw7IYQQQojLj2XZHGjsx7LtjMbvPtxDXtCDyzl/WeySWoG9//77uf/++yfdbxgGBw4c4PrrrycYDBIMBvH5fPMwQyGEEEKIy4tpWgxF4hmPD48kgczC7oVySQXYRx55hM985jOT7n/zzTexbZs1a9bg8Xi48847aWtrm/Y4iUSCcDg84UsIIYQQQkymqAoOPfPVVF1TyXCx9oK56AH2vvvuS6+inv71zW9+k9ra2imfc/jwYZYvX85PfvITDh06hMPh4IEHHpj2NR566CECgUD6q6Ki4kK9HSGEEEKIBU3XVBZX52Q8vr4yiEOf3zVQxbYvbobu6ekhFotNuj83Nxe/3z82KUWhubmZ6urqKY/R2tpKbW0tQ0ND6eecLpFIkEgk0rfD4TAVFRWEQqEpxwshhBBCXMlShsV//PogQ+HEjON0TeH33rcGp2Pu61/D4TCBQCCjvHbRL+IqKio672MEg0Esy6Krq2vKN+hyuXC5XOf9OkIIIYQQVwJVhXtuqucnjx+etk2WosCdW2q4BPYxuLRqYKfz2c9+lkcffTR9e8eOHaiqKqUBQgghhBBzQFNVgtkuPnD3MiqKJ18onx/08K5bF1FTFjiretkL5ZJqozWdNWvW8Gd/9mcUFxdjGAaf/vSn+chHPoLX653vqQkhhBBCXBZ0XSXH7+Lem+uJJ430VrKFuV4CPheqqqCpl8ba54IIsB/60Ic4fPgw9957Lz6fj3e+85189atfne9pCSGEEEJcVhRFwenQcDo0/LWXbjnmRb+Iaz6cTVGwEEIIIYS4+M4mr10a68BCCCGEEEJkSAKsEEIIIYRYUCTACiGEEEKIBUUCrBBCCCGEWFAkwAohhBBCiAVFAqwQQgghhFhQJMAKIYQQQogFRQKsEEIIIYRYUCTACiGEEEKIBUUCrBBCCCGEWFAkwAohhBBCiAVFAqwQQgghhFhQ9PmewMVg2zYA4XB4nmcihBBCCCGmMp7TxnPbTK6IABuJRACoqKiY55kIIYQQQoiZRCIRAoHAjGMUO5OYu8BZlsXJkyfx+XwoijIvcwiHw1RUVNDe3o7f75+XOVxO5HzOLTmfc0vO59yTczq35HzOLTmfc8O2bSKRCKWlpajqzFWuV8QKrKqqlJeXz/c0APD7/fLNPYfkfM4tOZ9zS87n3JNzOrfkfM4tOZ/nb7aV13FyEZcQQgghhFhQJMAKIYQQQogFRQLsReJyufjLv/xLXC7XfE/lsiDnc27J+Zxbcj7nnpzTuSXnc27J+bz4roiLuIQQQgghxOVDVmCFEEIIIcSCIgFWCCGEEEIsKBJghRBCCCHEgiIBVggh5sDAwABbt26lv79/vqcihBCXPQmwF9iBAwfYuHEjOTk5fO5zn8tof18x2cDAADU1NbS0tKTvk3N77n75y19SW1uLrutcffXVHD58GJBzeq5++tOfUl9fz4MPPkhlZSU//elPATmfc+HOO+/k+9//PiDn81x9+tOfRlGU9Fd9fT0g53Mu/Mmf/An33HNP+rac04tHAuwFlEgkuOeee1i/fj07d+7k0KFD6X+IReb6+/u5++67J4RXObfn7sSJE3z0ox/la1/7Gp2dnVRVVfHxj39czuk5Gh4e5tOf/jSvvPIKe/bs4Tvf+Q6f//zn5XzOgR/96Ec89dRTgPydPx+7du3i8ccfZ2hoiKGhIfbs2SPncw4cOHCAb33rW3zjG98A5Hv0orPFBfPYY4/ZOTk5djQatW3btvfu3Wtv3rx5nme18Nxyyy32N77xDRuwm5ubbduWc3s+fv3rX9sPP/xw+vbzzz9vO51OOafnqK2tzf6P//iP9O19+/bZPp9Pzud5GhgYsIuKiuzFixfb//Zv/ybn8xylUinb5/PZkUhkwv1yPs+PZVn2pk2b7D//8z9P3yfn9OKSFdgLaN++fVxzzTV4vV4AVq1axaFDh+Z5VgvPI488wmc+85kJ98m5PXd33303v/d7v5e+ffToUerr6+WcnqOKigo+8IEPAJBKpfj7v/973vWud8n5PE9/9Ed/xDvf+U6uueYaQP7On6s333wT27ZZs2YNHo+HO++8k7a2Njmf5+lf/uVf2Lt3LzU1NfzmN78hlUrJOb3IJMBeQOFwmJqamvRtRVHQNI2hoaF5nNXCU1tbO+k+ObdzI5lM8vd///d86lOfknN6nvbt20dRURFPP/003/jGN+R8nocXXniB5557jr/5m79J3yfn89wcPnyY5cuX85Of/IRDhw7hcDh44IEH5Hyeh5GREb74xS/S0NBAR0cHX//617n++uvlnF5kEmAvIF3XJ20r53a7GR0dnacZXT7k3M6NL37xi2RnZ/PJT35Szul5WrVqFc899xzLly/nox/9qJzPcxSPx3nggQd4+OGH8fv96fvlfJ6bD3zgA2zbto2NGzdSU1PDN7/5TZ5++mksy5LzeY5+/vOfE41Gef755/nzP/9znn76aYaHh/ne974n5/QikgB7AeXm5tLX1zfhvkgkgtPpnKcZXT7k3J6/Z555hm9/+9v8+Mc/xuFwyDk9T4qisHbtWr7//e/zy1/+Us7nOfryl7/Mxo0befvb3z7hfjmfcyMYDGJZFsXFxXI+z1FHRwdXX301ubm5wNgvV6tWrSIej8s5vYgkwF5AGzduZNu2benbLS0tJBKJ9De9OHdybs9PU1MTH/jAB3j44YdZtmwZIOf0XD3//PN87nOfS9/WdR2AJUuWyPk8Bz/+8Y/55S9/STAYJBgM8uMf/5hPfepT/OAHP5DzeQ4++9nP8uijj6Zv79ixA1VVWblypZzPc1RRUUEsFptwX2trK//wD/8g5/Rimu+ryC5nqVTKLigosH/wgx/Ytm3bDzzwgH333XfP86wWLk7rQiDn9tyNjo7aS5cutT/xiU/YkUgk/ZVMJuWcnoPOzk7b5/PZ3/nOd+y2tjb7Qx/6kH3HHXfI9+g5am9vt5ubm9Nf7373u+2/+7u/s/v6+uR8noMf/OAHdn19vf3SSy/Zzz33nL1kyRL7Yx/7mHx/noeBgQE7EAjYDz/8sN3e3m7/0z/9k+1yuezGxkY5pxeRBNgL7LHHHrM9Ho9dWFho5+Xl2QcOHJjvKS1YpwdY25Zze64ee+wxG5j01dzcLOf0HD355JP20qVLbZ/PZ7/nPe+xe3t7bduW79G58OEPf9j+t3/7N9u25Xyeqz/5kz+xg8GgXVFRYf/+7/++PTIyYtu2nM/z8frrr9ubNm2yPR6PXVNTYz/22GO2bcs5vZgU25ZtIi60zs5Odu7cyaZNmygoKJjv6VxW5NzOPTmnc0vO59yS8zm35HzOPTmnF4cEWCGEEEIIsaDIRVxCCCGEEGJBkQArhBBCCCEWFAmwQgghhBBiQZEAK4QQQgghFhQJsEIIIYQQYkGRACuEEJegF198EUVRUBQFh8PBypUreeqppwAYHh7mPe95D1lZWaxbt46dO3dOeO7AwAA1NTW0tLTMw8yFEOLC0+d7AkIIIabm9/tpbW0lFovxm9/8hve85z0cPXqUBx98kHg8zt69e3nmmWd4xzvewYkTJ/B4PPT393PPPfdIeBVCXNZkBVYIIS5RiqIQDAYpKSnhE5/4BNXV1fzgBz/gl7/8Jd/73vdoaGjgU5/6FA6Hg+effx6A+++/n/vvv3+eZy6EEBeWBFghhFggdF2nrKyM2tpaSkpK0vc/+OCDBAIBAB555BE+85nPzNcUhRDiopASAiGEWACeeeYZjh49SldXF0VFRRMe++M//uP0/9fW1l7sqQkhxEUnAVYIIS5RoVCIYDBIPB7H7XbzrW99i46ODjRNm++pCSHEvJISAiGEuET5fD727t3LiRMnGBoa4iMf+QjBYJChoaEJ4zZt2sTDDz88T7MUQoiLT1ZghRDiEqWqKtXV1RPuW7t2LceOHSMcDuP3+wFobm6msrJyHmYohBDzQ1ZghRBiAdm0aRPLly/nk5/8JE1NTXzlK18hlUpx4403zvfUhBDiopEAK4QQC4iiKPz6178mHA6zfPlyHnvsMZ544gmysrLme2pCCHHRKLZt2/M9CSGEEEIIITIlK7BCCCGEEGJBkQArhBBCCCEWFAmwQgghhBBiQZEAK4QQQgghFhQJsEIIIYQQYkGRACuEEEIIIRYUCbBCCCGEEGJBkQArhBBCCCEWFAmwQgghhBBiQZEAK4QQQgghFpT/H2UgEJtVLwwzAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.rcParams['font.sans-serif']=['SimHei']#显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False\n",
    "x = p1.iloc[:,[9,11,12,13]]\n",
    "x = x.dropna()\n",
    "ss = StandardScaler()\n",
    "train_ss_x = ss.fit_transform(x)\n",
    "pca = PCA(n_components  = 2)\n",
    "#将四个维度降低至两个维度，便于人眼进行识别，即降维就是在保证“最重要信息”不丢失的前提下，把多维度的数据压缩到 2D/3D，让我们既能用算法处理，也能用图形直观展示。\n",
    "pca_result = pca.fit_transform(train_ss_x)\n",
    "#将标准化后数据进行降维\n",
    "df_plot = pd.DataFrame(pca_result, columns=['PC1', 'PC2'])\n",
    "#定义DataFrame将降维后的数据以两列数据进行展示\n",
    "df_plot['cluster'] = predict_y\n",
    "#定义cluster列显示聚类后结果\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.scatterplot(data=df_plot, x='PC1', y='PC2', hue='cluster', palette='Set2', s=80)\n",
    "#绘制散点图\n",
    "plt.title('KMeans 聚类结果可视化 (PCA降维后)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "3f01aa1d-48b0-4058-be2c-8a5ae24676ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "四个维度线性组合占比如下所示：\n",
      "            0         1         2         3\n",
      "PC1  0.159291  0.710319  0.684112 -0.045432\n",
      "PC2  0.951117  0.037359 -0.269906 -0.145367\n",
      "各主成分解释的方差比： [0.44760116 0.25515132]\n"
     ]
    }
   ],
   "source": [
    "train_ss_x = pd.DataFrame(train_ss_x)\n",
    "#把序列train_ss_x转化为DataFrame\n",
    "loadings = pd.DataFrame(\n",
    "    pca.components_,\n",
    "    columns = train_ss_x.columns,\n",
    "    index=['PC1', 'PC2']\n",
    ")\n",
    "print('四个维度线性组合占比如下所示：')\n",
    "print(loadings)\n",
    "print(\"各主成分解释的方差比：\", pca.explained_variance_ratio_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10069741-cb0d-442d-9e67-ae3e7adc2b07",
   "metadata": {},
   "source": [
    "### **降维之后，两个主维度是由四个维度进行线性组合而成，以上显示四个维度线性组合的占比。**"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:base] *",
   "language": "python",
   "name": "conda-base-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
